WP22 v1.0: Monte Carlo, revisions, benchmarks, full manuscript (49 p.)
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
34 changed files +3,052 −120
modified
README.md
+14 −3
@@ -3,7 +3,7 @@ | ||
| 3 | 3 | |
| 4 | 4 | **UQO Working Paper No. 22** — *Monthly municipal indices for Quebec from 770,000 registered sales, 2021–2026* |
| 5 | 5 | |
| 6 | −[](paper/main.pdf) | |
| 6 | +[-b31b1b?logo=latex&logoColor=white)](paper/main.pdf) | |
| 7 | 7 | [](requirements.txt) |
| 8 | 8 | [](data/raw/README.md) |
| 9 | 9 | [](scripts/) |
@@ -19,7 +19,7 @@ | ||
| 19 | 19 | | Item | Value | |
| 20 | 20 | |---|---| |
| 21 | 21 | | Version | 1.0 (October 2026) | |
| 22 | −| SSRN | not yet submitted | | |
| 22 | +| SSRN | [7651200](https://ssrn.com/abstract=7651200) — submitted 2026-10-21 (PRELIMINARY_UPLOAD) | | |
| 23 | 23 | | Journal | — | |
| 24 | 24 | | Code & data | reproducible from `scripts/` (see Quick start); raw microdata not redistributed | |
| 25 | 25 | |
@@ -36,7 +36,18 @@ Every ingredient exists in the literature (Francke & van de Minne 2017; Ishaak, | ||
| 36 | 36 | |
| 37 | 37 | ## 🏆 Headline results |
| 38 | 38 | |
| 39 | −See `paper/tables/numbers.tex` (every number in the manuscript is a generated macro). Key outputs: `data/processed/index_panel.parquet|csv` (schema of protocol §17), `results/panel/reliability_cells.csv`, `results/simulation/summary.csv`, `results/vintages/revisions_by_release.csv`, `results/validation/*.csv`. | |
| 39 | +| Object | Estimate | | |
| 40 | +|---|---| | |
| 41 | +| Provincial index, Sept. 2026 (2021 avg = 100): houses / condos / plexes | 154.9 / 136.4 / 151.2 | | |
| 42 | +| Municipal house index, Sept. 2026, grades A–C: P10 / median / P90 | 133 / 153 / 174 | | |
| 43 | +| Monte Carlo (baseline, municipalities): RMSE of monthly log change ×100 — SHARP / per-cell state space (QHPI) / cell time dummy / median | 1.58 / 2.91 / 12.52 / 24.28 | | |
| 44 | +| Monte Carlo coverage of 95 % intervals, municipalities: SHARP / without EB calibration | 98 % / 97 % | | |
| 45 | +| Counter-examples | heavy tails: municipal-level location FE wins (2.84 vs 4.62); regional level: QHPI direct local TD wins (1.17 vs 1.40) | | |
| 46 | +| Composition shock (drop smallest 40 % of houses from 2024, Montréal region): median / TD / SHARP shift | +23.4 / -0.5 / +0.3 log pts | | |
| 47 | +| First-release revision of monthly change (houses): province / municipalities (full re-estimation) / municipalities (frozen) | 0.16 / 1.72 / 2.31 log pts | | |
| 48 | +| External: Québec CMQ houses 2021→2026 SHARP / CREA single-family / Teranet | 47.9 / 45.5 / 43.7 log pts | | |
| 49 | + | |
| 50 | +Every number in the manuscript is a generated macro (`paper/tables/numbers.tex`). Key outputs: `data/processed/index_panel.parquet|csv` (schema of protocol §17), `results/panel/reliability_cells.csv`, `results/simulation/summary.csv`, `results/vintages/revisions_by_release*.csv`, `results/validation/*.csv`. | |
| 40 | 51 | |
| 41 | 52 | ## 🗂 Repository layout |
| 42 | 53 | |
modified
docs/research_log.md
+1 −0
@@ -14,3 +14,4 @@ | ||
| 14 | 14 | | 01:25 | 6 | Real-time vintages (July 2022 → October 2026, 10-day delay) launched; validation: aggregation, composition shock, placebo, Moran, repeat sales, bootstrap, ablations, trimming | |
| 15 | 15 | | 01:30 | 6 | External indices (Teranet, CREA, StatCan) obtained by agent; comparison on annual means; Montréal gap diagnosed (type composition, richer CREA attributes); Québec City agreement | |
| 16 | 16 | | 01:40 | 7 | Manuscript sections 1–6, 8, 10–11 and appendices drafted; figure and table generators; tests (9 passing) | |
| 17 | +| 14:00–16:30 | 4–7 | Laptop slept 01:38→13:43 (battery); Monte Carlo resumed and completed (720 reps); vintages regenerated with filtered + smoothed modes; benchmarks and HMTS completed; sections 7 and 9 written; 49-page manuscript compiles with 0 missing macros | | |
added
figures/fig_mc_coverage.pdf
+0 −0
Binary file not shown.
added
figures/fig_mc_coverage.png
+0 −0
Binary file not shown.
added
figures/fig_mc_rmse.pdf
+0 −0
Binary file not shown.
added
figures/fig_mc_rmse.png
+0 −0
Binary file not shown.
added
figures/fig_revisions.pdf
+0 −0
Binary file not shown.
added
figures/fig_revisions.png
+0 −0
Binary file not shown.
added
paper/figures/fig_mc_coverage.pdf
+0 −0
Binary file not shown.
added
paper/figures/fig_mc_rmse.pdf
+0 −0
Binary file not shown.
added
paper/figures/fig_revisions.pdf
+0 −0
Binary file not shown.
modified
paper/main.pdf
+0 −0
Binary file not shown.
added
paper/main_anon.pdf
+0 −0
Binary file not shown.
modified
paper/sections/abstract.tex
+1 −1
@@ -1,3 +1,3 @@ | ||
| 1 | 1 | \newcommand{\WPabstract}{% |
| 2 | −This paper develops and evaluates SHARP-HPI, a framework for monthly quality-adjusted housing price indices at the municipal level in thin markets. Stage one is a robust pooled hedonic regression with fine-scale location fixed effects, smoothly time-varying quality prices and provincial month effects; stage two models each geography's deviation from its parent (province, administrative region, regional county municipality, municipality) with heteroskedastic local-level state-space models whose innovation variances are calibrated by empirical Bayes across cells, so that the weight given to local sales adapts to their reliability. We apply it to \NRawTx{} registered sales in Quebec from January 2021 to October 2026 matched to the assessment roll, with an audited population definition (\NHeadline{} sales), a measured publication lag and real-time vintages. Against eleven benchmark estimators (median, stratified median, pooled and rolling time dummies, hedonic imputation, GEKS, HMTS, repeat sales, gradient boosting and a production hierarchical index), Monte Carlo experiments on synthetic Quebec-like markets with known latent paths show where the adaptive hierarchy reduces the error of monthly changes and interval under-coverage, and two deliberate counter-examples where it does not. The empirical panel covers \NCells{} geography--class cells with reliability grades, effective sample sizes and local-data weights; aggregates agree closely with Teranet and CREA indices in Québec City and diverge by an explainable margin in Montréal. Every ingredient of the estimator exists in the literature; the contribution is their integration, the explicit reliability accounting and the documented performance.% | |
| 2 | +This paper develops and evaluates SHARP-HPI, a framework for monthly quality-adjusted housing price indices at the municipal level in thin markets. Stage one is a robust pooled hedonic regression with fine-scale location fixed effects, smoothly time-varying quality prices and provincial month effects; stage two models each geography's deviation from its parent (province, administrative region, regional county municipality, municipality) with heteroskedastic local-level state-space models whose innovation variances are calibrated by empirical Bayes across cells, so that the weight given to local sales adapts to their reliability. We apply it to \NRawTx{} registered sales in Quebec from January 2021 to October 2026 matched to the assessment roll, with an audited population definition (\NHeadline{} sales), a measured publication lag and real-time vintages. Against eleven benchmark estimators (median, stratified median, pooled and rolling time dummies, hedonic imputation, GEKS, HMTS, repeat sales, gradient boosting and a production hierarchical index), Monte Carlo experiments on synthetic Quebec-like markets with known latent paths show that the adaptive hierarchy halves the error of municipal monthly changes relative to per-cell state-space estimation and keeps interval coverage near nominal, and two deliberate counter-examples show where it does not: heavy-tailed contamination favours a coarser location partition, and at the regional level region-specific hedonic coefficients beat pooled coefficients plus a smoothed deviation. The empirical panel covers \NCells{} geography--class cells with reliability grades, effective sample sizes and local-data weights; aggregates agree closely with Teranet and CREA indices in Québec City and diverge by an explainable margin in Montréal. Every ingredient of the estimator exists in the literature; the contribution is their integration, the explicit reliability accounting and the documented performance.% | |
| 3 | 3 | } |
modified
paper/sections/conclusion.tex
+1 −1
@@ -6,4 +6,4 @@ The data limit what any estimator can do. Characteristics come from a single cur | ||
| 6 | 6 | |
| 7 | 7 | \subsection{Conclusion} |
| 8 | 8 | |
| 9 | −We set out to measure constant-quality house price movements where the data are thin, and to say how far they can be trusted. The framework integrates known components; the evidence shows that their combination produces municipal monthly indices with errors a fraction of those of medians and cell-level time dummies in simulation, intervals whose coverage is close to nominal when the innovation variance is calibrated across cells, a revision profile that is measured rather than designed away, and aggregates that agree with external indices where the scopes match. It also shows where the approach should not be used: in markets with strong idiosyncratic local dynamics the pooled estimator lags a direct one, and for cells with fewer than fifteen effective observations no estimator reports more than a model-assisted interpolation. The panel, the vintage database and the code are released so that both claims can be checked. | |
| 9 | +We set out to measure constant-quality house price movements where the data are thin, and to say how far they can be trusted. The framework integrates known components; the evidence shows that their combination produces municipal monthly indices with errors a fraction of those of medians and cell-level time dummies in simulation, intervals whose coverage is close to nominal when the innovation variance is calibrated across cells, a revision profile that is measured rather than designed away, and aggregates that agree with external indices where the scopes match. It also shows where the approach should not be used: at the regional level, where sales are plentiful, region-specific hedonic coefficients are preferable to pooled coefficients plus a deviation; with heavy-tailed contamination a coarser location partition is more robust than fine cells of fifteen sales; in markets with strong idiosyncratic local dynamics the real-time estimate lags; and for cells with fewer than fifteen effective observations no estimator reports more than a model-assisted interpolation. The panel, the vintage database and the code are released so that both claims can be checked. | |
modified
paper/sections/introduction.tex
+1 −1
@@ -8,6 +8,6 @@ The paper makes four contributions. First, it builds the index on an audited pop | ||
| 8 | 8 | |
| 9 | 9 | We are explicit about novelty. The verified literature (Section~\ref{sec:lit}) contains every ingredient: structural time-series index levels \citep{francke2010thin}, hierarchical common-plus-deviation trends with heavy-tailed errors \citep{francke2017hierarchical}, small-area shrinkage with estimated weights \citep{fayherriot1979,raomolina2015}, rolling-window hedonics with low-volume safeguards \citep{hill2022rtd}, and hedonic multilateral time-series smoothing for small domains in official statistics \citep{ishaak2024hmts}. What we did not find in a single published estimator is the combination of a single-sale hedonic observation equation, a multi-level administrative hierarchy, empirical-Bayes calibration of the pooling strength across cells, reliability accounting for every cell-month, and real-time vintage evaluation with a measured registration lag. The paper should be read as an integration and empirical assessment of these components, not as a new class of model. |
| 10 | 10 | |
| 11 | −Three findings matter for practice. In the Monte Carlo experiments the hierarchical state-space estimators reduce the root mean squared error of municipal monthly changes by a large factor relative to medians and cell-level time dummies, and the empirical-Bayes calibration of the innovation variance improves interval coverage relative to per-cell maximum likelihood; the local-linear-trend variant and the Gaussian variant do not improve on the local-level robust variant, and in markets with strong idiosyncratic municipal dynamics the pooled estimator is slower than a direct local estimate. On the Quebec data the provincial house index rises from 100 in 2021 to \ProvSephouse{} in September 2026, condominiums to \ProvSepcondo{} and plexes to \ProvSepplex{}; the dispersion across municipalities is wide and largely reliable only for cells graded A to C. In real time, first-release monthly changes at the provincial level are revised by a mean absolute \RevFirsthouseprovince{} log points (houses), and municipal first releases by \RevFirsthousemunicipality{}, which bounds the gain from any non-revising splice policy. | |
| 11 | +Three findings matter for practice. In the Monte Carlo experiments the hierarchical state-space estimator reduces the root mean squared error of municipal monthly changes to \MCmunicipalitySsharpbaselineRmse{} log points, against \MCmunicipalityKqhpibaselineRmse{} for per-cell state-space estimation, \MCmunicipalityCpooledtdbaselineRmse{} for a cell-level time dummy and \MCmunicipalityAmedianbaselineRmse{} for the median; the empirical-Bayes calibration of the innovation variance matters for interval coverage rather than for point accuracy; the local-linear-trend and Gaussian variants do not improve on the robust local level; and two counter-examples show where the design loses: with heavy-tailed contamination a coarser location partition is more robust, and at the regional level, where sales are plentiful, estimating each region's own hedonic coefficients beats pooled coefficients plus a smoothed deviation. On the Quebec data the provincial house index rises from 100 in 2021 to \ProvSephouse{} in September 2026, condominiums to \ProvSepcondo{} and plexes to \ProvSepplex{}; the dispersion across municipalities is wide and largely reliable only for cells graded A to C. In real time, first-release monthly changes at the provincial level are revised by a mean absolute \RevFirsthouseprovince{} log points (houses), and municipal first releases by \RevFirsthousemunicipality{}; publishing without revision costs about half a log point per month at the municipal level. | |
| 12 | 12 | |
| 13 | 13 | The remainder of the paper follows the protocol in which it was pre-specified. Section~\ref{sec:lit} reviews the literature and states the gap. Section~\ref{sec:data} describes the data, the institutional setting and the population definition. Section~\ref{sec:estimand} defines the estimand and the measurement challenges. Section~\ref{sec:framework} presents the SHARP-HPI model and identification. Section~\ref{sec:estimation} covers estimation, inference, linking and vintages. Section~\ref{sec:simulation} reports the Monte Carlo evidence. Section~\ref{sec:empirical} presents the Quebec indices, Section~\ref{sec:validation} the benchmarks, validation, ablations and robustness, Section~\ref{sec:implications} the implications, and Section~\ref{sec:conclusion} the limitations and conclusion. Code, the index panel, the vintage database and every table and figure are generated by the accompanying repository. |
modified
paper/sections/simulation.tex
+35 −1
@@ -1,3 +1,37 @@ | ||
| 1 | 1 | \section{Simulation design and Monte Carlo results}\label{sec:simulation} |
| 2 | −% filled in after the Monte Carlo run completes (scripts/20_simulation.py) | |
| 2 | + | |
| 3 | +\subsection{Design} | |
| 4 | + | |
| 5 | +Synthetic markets reproduce the features of the Quebec data that matter for index estimation (Appendix~\ref{app:sim}): 60 municipalities nested in 12 MRCs and 4 regions, expected monthly sales drawn from a log-normal distribution with a median of 3.3 sales (a quarter of the municipalities sell less than once a month, a few sell more than 50 times), latent quality-constant log price paths with nested random-walk deviations, a hedonic surface with a nonlinear floor-area effect and an optional drifting elasticity, spatially correlated cell quality, Student-$t$ noise with contamination, June--July seasonality in volumes, resales, missing floor areas and registration lags. Nine scenarios (Table~\ref{tab:mc_design}) vary the data-generating process: a baseline; a turning point at month 36; a flat market in which the floor area of sold homes trends upward (the median must rise spuriously); a rising market in which sold-home quality declines; drifting hedonic prices with increasing missingness; heavy tails (2.5 degrees of freedom, 4\% contamination); a sparse market with 35\% of the sales; and two deliberate counter-examples, a market without local dynamics (where pooling should dominate) and a market with strong idiosyncratic municipal dynamics (where pooling should hurt and a direct estimator should win). Each scenario is replicated 80 times with new seeds; every estimator of Section~\ref{sec:validation} that can be computed on a municipality is run on every cell, and SHARP-HPI is run in its full form and in five ablations (no empirical-Bayes pooling, no time-varying prices, local linear trend, Gaussian innovations, municipal-level location effects), in smoothed and filtered modes. The truth for a municipality is its latent path; for a region or the province it is the stock-weighted mean of its municipalities' paths. Reported metrics are averaged over cells and replications with Monte Carlo standard errors over replications (Tables~\ref{tab:mc_rmse_municipality}--\ref{tab:mc_lag}, Figures~\ref{fig:mc_rmse}--\ref{fig:mc_cov}). | |
| 6 | + | |
| 3 | 7 | \input{tables/tab_mc_design} |
| 8 | + | |
| 9 | +\subsection{Accuracy of monthly changes} | |
| 10 | + | |
| 11 | +At the municipal level the ranking is unambiguous and stable across scenarios (Table~\ref{tab:mc_rmse_municipality}, Figure~\ref{fig:mc_rmse}). In the baseline the RMSE of the monthly log change is \MCmunicipalitySsharpbaselineRmse{} log points for SHARP-HPI, \MCmunicipalityKqhpibaselineRmse{} for the QHPI architecture (per-cell maximum-likelihood deviations), \MCmunicipalityCpooledtdbaselineRmse{} for a cell-level pooled time dummy, \MCmunicipalityBstratifiedbaselineRmse{} for the stratified median and \MCmunicipalityAmedianbaselineRmse{} for the median; repeat sales are estimable in only \MCmunicipalityHrepeatsalesbaselineShare\% of the municipal cell-months and are the noisiest where they exist. The composition scenarios show what quality adjustment buys: in the flat market with rising sold quality the median's monthly changes carry a bias of \MCmunicipalityAmedianflatqualityupBias{} log points per month (about 3\% per year of spurious appreciation), the hedonic estimators' \MCmunicipalitySsharpflatqualityupBias{}. The real-time (filtered) SHARP-HPI has an RMSE of \MCmunicipalitySsharpfilteredbaselineRmse{}, twice the smoothed value: half of the final accuracy at the municipal level comes from information that arrives after the month, which is the revision cost documented on the real data. | |
| 12 | + | |
| 13 | +\input{tables/tab_mc_rmse_municipality} | |
| 14 | + | |
| 15 | +\begin{figure}[htbp]\centering | |
| 16 | +\includegraphics[width=\textwidth]{figures/fig_mc_rmse.pdf} | |
| 17 | +\caption{Monte Carlo: RMSE of monthly log changes (log scale) at the municipality and region levels by scenario and method; whiskers are Monte Carlo standard errors.}\label{fig:mc_rmse} | |
| 18 | +\end{figure} | |
| 19 | + | |
| 20 | +\subsection{What each component contributes} | |
| 21 | + | |
| 22 | +The ablations identify the mechanisms. Empirical-Bayes calibration of the innovation variance improves the RMSE only slightly in the baseline (\MCmunicipalitySnoEBbaselineRmse{} without it) but matters for calibration: the 95\% intervals cover \MCmunicipalitySsharpbaselineCov\% of the true levels with it and \MCmunicipalitySnoEBbaselineCov\% without, and in the heavy-tailed scenario \MCmunicipalitySnoEBheavytailsCov\% against \MCmunicipalitySsharpheavytailsCov\%, because per-cell maximum likelihood collapses to the boundary in thin cells. The local-linear-trend variant is never better than the local level and doubles the revision between filtered and smoothed estimates (\MCmunicipalitySLLTbaselineRev{} against \MCmunicipalitySsharpbaselineRev{} log points). Gaussian innovations cost \MCmunicipalitySnorobustbaselineRmse{} against \MCmunicipalitySsharpbaselineRmse{} in the baseline and \MCmunicipalitySnorobustheavytailsRmse{} against \MCmunicipalitySsharpheavytailsRmse{} with heavy tails. Fine location effects matter in every scenario but one: replacing them by municipal effects raises the RMSE to \MCmunicipalityScoarselocbaselineRmse{} in the baseline. The exception is the heavy-tailed scenario, the first counter-example, where the municipal-location variant is the best estimator (\MCmunicipalityScoarselocheavytailsRmse{} against \MCmunicipalitySsharpheavytailsRmse): with 4\% contamination and 2.5 degrees of freedom, fine cells of fifteen sales absorb outliers into their fixed effects and transmit them to the residual means, and a coarser partition is more robust. Time-varying quality prices change nothing in the baseline and help marginally when prices drift. | |
| 23 | + | |
| 24 | +\subsection{Counter-examples and the regional level} | |
| 25 | + | |
| 26 | +The second counter-example, strong idiosyncratic municipal dynamics, did not produce the reversal it was designed for at the municipal level: the smoothed SHARP-HPI remains the most accurate (\MCmunicipalitySsharpidiosyncratictownsRmse{} against \MCmunicipalityKqhpiidiosyncratictownsRmse{} for per-cell estimation) because the empirical-Bayes prior is wide enough to let volatile cells keep large innovation variances. The cost appears in real time: the filtered estimate lags the truth by \MCmunicipalitySsharpfilteredidiosyncratictownsLag{} months by cross-correlation, against \MCmunicipalitySsharpfilteredbaselineLag{} in the baseline, and its coverage falls to \MCmunicipalitySsharpfilteredidiosyncratictownsCov\%. The regional level (Table~\ref{tab:mc_rmse_region}) is where SHARP-HPI's design costs accuracy: with 60 to 1{,}000 sales a month per region, the QHPI architecture, which estimates each liquid region with its own hedonic coefficients and smooths lightly, has an RMSE of \MCregionKqhpibaselineRmse{} against \MCregionSsharpbaselineRmse{} for SHARP-HPI, whose regional path is the pooled provincial path plus a smoothed deviation; the Monte Carlo confirms the repeat-sales evidence of Section~\ref{sec:validation} that region-specific estimation is preferable where the data allow it, and the panel's regional series should be read with that caveat. At the provincial level all hedonic estimators agree within 0.1 log point and the medians are three times noisier (Table~\ref{tab:mc_rmse_province}); the stock-weighted bottom-up aggregate of municipal SHARP indices is as accurate as the direct provincial estimate. | |
| 27 | + | |
| 28 | +\input{tables/tab_mc_rmse_region} | |
| 29 | +\input{tables/tab_mc_rmse_province} | |
| 30 | +\input{tables/tab_mc_coverage} | |
| 31 | +\input{tables/tab_mc_bias} | |
| 32 | +\input{tables/tab_mc_lag} | |
| 33 | + | |
| 34 | +\begin{figure}[htbp]\centering | |
| 35 | +\includegraphics[width=\textwidth]{figures/fig_mc_coverage.pdf} | |
| 36 | +\caption{Monte Carlo: empirical coverage of nominal 95\% intervals for municipal index levels.}\label{fig:mc_cov} | |
| 37 | +\end{figure} | |
modified
paper/sections/validation.tex
+14 −3
@@ -2,7 +2,7 @@ | ||
| 2 | 2 | |
| 3 | 3 | \subsection{Benchmarks at the provincial and regional levels}\label{sec:bench} |
| 4 | 4 | |
| 5 | −Table~\ref{tab:benchmarks_province} reports every benchmark at the provincial level: the cumulative log change from January 2021 to September 2026 and the standard deviation of monthly log changes, with wall-clock times. Figure~\ref{fig:provmethods} plots the series. Four facts stand out. First, the quality-adjusted methods agree on the magnitude of the house appreciation: pooled time dummies, rolling time dummies with any window and splice, hedonic imputation, GEKS, HMTS, the QHPI architecture and SHARP-HPI all lie between \ProvDrtdwOneThreemovementhouseTotal{} and \ProvGhmtswOneThreehouseTotal{} log points, against \ProvAmedianhouseTotal{} for the median and \ProvHrepeatsalescshouseTotal{} for Case--Shiller repeat sales. The median understates because the sold houses became smaller and older over the sample; repeat sales overstate because resold houses are renovated. Second, the chained imputation indices drift relative to their direct counterparts for condominiums and plexes (Laspeyres \ProvEhilaspeyreschainedcondoTotal{} against Paasche \ProvEhipaaschechainedcondoTotal{} for condominiums), the classical chain-drift of monthly chaining with per-period hedonic functions estimated on a few thousand sales. Third, methods differ far more in month-to-month volatility than in trend: the standard deviation of monthly changes is about 1.9 log points for the time-dummy family and SHARP, 2.8 for imputation and GEKS, 3--4 for the median and repeat sales, and 1.3--1.9 for HMTS, whose state-space smoothing of each base-period series is visible. Fourth, the gradient-boosting residual index, which forecasts each month's prices from models trained on earlier months, rises by only \ProvJgbmootresidualhouseTotal{} log points: a predictive model that learns location and time jointly absorbs part of the appreciation into its fitted surface, which is why predictive accuracy is not index accuracy. SHARP-HPI with and without time-varying quality prices differ by less than one log point in total at the provincial level; the drift matters below the province. | |
| 5 | +Table~\ref{tab:bench_prov} reports every benchmark at the provincial level: the cumulative log change from January 2021 to September 2026 and the standard deviation of monthly log changes, with wall-clock times. Figure~\ref{fig:provmethods} plots the series. Four facts stand out. First, the quality-adjusted methods agree on the magnitude of the house appreciation: pooled time dummies, rolling time dummies with any window and splice, hedonic imputation, GEKS, HMTS, the QHPI architecture and SHARP-HPI all lie between \ProvDrtdwOneThreemovementhouseTotal{} and \ProvGhmtswOneThreehouseTotal{} log points, against \ProvAmedianhouseTotal{} for the median and \ProvHrepeatsalescshouseTotal{} for Case--Shiller repeat sales. The median understates because the sold houses became smaller and older over the sample; repeat sales overstate because resold houses are renovated. Second, the chained imputation indices drift relative to their direct counterparts for condominiums and plexes (Laspeyres \ProvEhilaspeyreschainedcondoTotal{} against Paasche \ProvEhipaaschechainedcondoTotal{} for condominiums), the classical chain-drift of monthly chaining with per-period hedonic functions estimated on a few thousand sales. Third, methods differ far more in month-to-month volatility than in trend: the standard deviation of monthly changes is about 1.9 log points for the time-dummy family and SHARP, 2.8 for imputation and GEKS, 3--4 for the median and repeat sales, and 1.3--1.9 for HMTS, whose state-space smoothing of each base-period series is visible. Fourth, the gradient-boosting residual index, which forecasts each month's prices from models trained on earlier months, rises by only \ProvJgbmootresidualhouseTotal{} log points: a predictive model that learns location and time jointly absorbs part of the appreciation into its fitted surface, which is why predictive accuracy is not index accuracy. SHARP-HPI with and without time-varying quality prices differ by less than one log point in total at the provincial level; the drift matters below the province. | |
| 6 | 6 | |
| 7 | 7 | \input{tables/tab_benchmarks_province} |
| 8 | 8 | |
@@ -11,7 +11,7 @@ Table~\ref{tab:benchmarks_province} reports every benchmark at the provincial le | ||
| 11 | 11 | \caption{Provincial indices by method (2021 average $=100$), January 2021 to September 2026.}\label{fig:provmethods} |
| 12 | 12 | \end{figure} |
| 13 | 13 | |
| 14 | −Table~\ref{tab:benchmarks_region} moves to the 16 administrative regions, where sample sizes range from 60 to 1{,}200 sales a month and the methods that estimate a separate hedonic function per region become unstable. The regional comparison has no truth; we report the volatility of each series and its root mean squared distance to the regional Case--Shiller index over 12-month changes. Three results are relevant to the design. The median and stratified median are two to three times as volatile as the hedonic methods and the farthest from repeat sales. Among hedonic methods, the QHPI architecture, which fits each liquid region with its own coefficients and smooths lightly, is the least volatile (\RegSdKqhpidirectlocaltdhouse{} for houses) and the closest to repeat sales (\RegRmseRSKqhpidirectlocaltdhouse); SHARP-HPI, whose regional path is the pooled provincial path plus a smoothed deviation, is less volatile than the pooled time dummy (\RegSdSsharphouse{} against \RegSdCpooledtdhouse) but not closer to repeat sales than it (\RegRmseRSSsharphouse{} against \RegRmseRSCpooledtdhouse). The reason is identifiable: with 400 to 1{,}200 sales a month the regional Kalman gain is close to one, so the SHARP regional path is essentially the pooled model's monthly regional mean, and the pooled model constrains quality prices to be common across regions. The Monte Carlo experiments of Section~\ref{sec:simulation}, where the truth is known, show that this costs little when quality prices are common and that regional estimates are then close to the truth; on the real data the repeat-sales arbiter, biased though it is, suggests that region-specific quality prices carry information at the regional level. Figure~\ref{fig:regmethods} shows six regions. | |
| 14 | +Table~\ref{tab:bench_region} moves to the 16 administrative regions, where sample sizes range from 60 to 1{,}200 sales a month and the methods that estimate a separate hedonic function per region become unstable. The regional comparison has no truth; we report the volatility of each series and its root mean squared distance to the regional Case--Shiller index over 12-month changes. Three results are relevant to the design. The median and stratified median are two to three times as volatile as the hedonic methods and the farthest from repeat sales. Among hedonic methods, the QHPI architecture, which fits each liquid region with its own coefficients and smooths lightly, is the least volatile (\RegSdKqhpidirectlocaltdhouse{} for houses) and the closest to repeat sales (\RegRmseRSKqhpidirectlocaltdhouse); SHARP-HPI, whose regional path is the pooled provincial path plus a smoothed deviation, is less volatile than the pooled time dummy (\RegSdSsharphouse{} against \RegSdCpooledtdhouse) but not closer to repeat sales than it (\RegRmseRSSsharphouse{} against \RegRmseRSCpooledtdhouse). The reason is identifiable: with 400 to 1{,}200 sales a month the regional Kalman gain is close to one, so the SHARP regional path is essentially the pooled model's monthly regional mean, and the pooled model constrains quality prices to be common across regions. The Monte Carlo experiments of Section~\ref{sec:simulation}, where the truth is known, show that this costs little when quality prices are common and that regional estimates are then close to the truth; on the real data the repeat-sales arbiter, biased though it is, suggests that region-specific quality prices carry information at the regional level. Figure~\ref{fig:regmethods} shows six regions. | |
| 15 | 15 | |
| 16 | 16 | \input{tables/tab_benchmarks_region} |
| 17 | 17 | |
@@ -76,7 +76,18 @@ Table~\ref{tab:trimming} shows that the provincial time-dummy path is insensitiv | ||
| 76 | 76 | |
| 77 | 77 | \subsection{Real-time vintages and revisions}\label{sec:revisions} |
| 78 | 78 | |
| 79 | −%%VINTAGES%% | |
| 79 | +Releases were simulated monthly from July 2022 to October 2026 for the province, the regions and the 64 municipalities with at least 1{,}500 sales, with the ten-day publication delay measured in Section~\ref{sec:data}; at each release both stages are re-estimated on the data available then. Two publication modes are compared. Under \emph{full re-estimation} the published value of a month is the smoothed estimate given the data available at the release, so history is revised; under the \emph{frozen first release} the value of a month is its one-sided filtered estimate and never changes except through stage-one re-estimation, which is the state-space analogue of a movement splice. Table~\ref{tab:revisions} reports, for full re-estimation, the mean absolute revision of the monthly log change between the $k$-th release and the final retrospective series; Table~\ref{tab:revisions_frozen} the same for the frozen policy; Table~\ref{tab:policies} the provincial policies; Figure~\ref{fig:revisions} the profile and the fan of provincial vintages. | |
| 80 | + | |
| 81 | +At the provincial level the first-release monthly change is within \PolFirsthouse{} log points of the final value for houses (\PolFirstcondo{} for condominiums, \PolFirstplex{} for plexes), about a tenth of the typical monthly change (\PolMeanAbshouse{}), and the third release is no closer: the provincial revision comes entirely from stage-one re-estimation with more data, not from the latest month, so a frozen policy loses nothing at the province. At the regional and municipal levels the two modes differ. For houses the first-release municipal monthly change is revised by \RevFirsthousemunicipality{} log points under full re-estimation and the gap to the final series under the frozen policy is \FrozenFirsthousemunicipality; for condominiums \RevFirstcondomunicipality{} against \FrozenFirstcondomunicipality. The revision declines over the first six releases as the smoother incorporates later months and then stabilises at the stage-one component; twelve-month changes are revised by \RevFirstTwelvehousemunicipality{} log points at first release for houses. The frozen policy therefore costs about half a log point per month at the municipal level relative to publishing revised values, the price of never revising, and the revision-limited policy (freeze at the third release) captures most of the gain of full re-estimation. These magnitudes are the timeliness--accuracy trade-off that a publisher of municipal indices faces; they are measured on this data and would differ with a slower registry. | |
| 82 | + | |
| 83 | +\input{tables/tab_revisions} | |
| 84 | +\input{tables/tab_revisions_frozen} | |
| 85 | +\input{tables/tab_policies} | |
| 86 | + | |
| 87 | +\begin{figure}[htbp]\centering | |
| 88 | +\includegraphics[width=\textwidth]{figures/fig_revisions.pdf} | |
| 89 | +\caption{Left: mean absolute revision of the monthly change by release number, houses (solid: full re-estimation; dashed: frozen first release). Right: every monthly vintage of the provincial house index, July 2022 to October 2026 (the darkest line is the latest).}\label{fig:revisions} | |
| 90 | +\end{figure} | |
| 80 | 91 | |
| 81 | 92 | \subsection{External comparison} |
| 82 | 93 | |
modified
paper/tables/numbers.tex
+2127 −12
@@ -214,6 +214,2121 @@ | ||
| 214 | 214 | \newcommand{\ExtSHARPhousestockweightedTeranetNSArepeatsalesQubecCMAExt}{43.7} |
| 215 | 215 | \newcommand{\ExtSHARPhousestockweightedTeranetNSArepeatsalesQubecCMAGap}{+4.3} |
| 216 | 216 | \newcommand{\ExtSHARPhousestockweightedTeranetNSArepeatsalesQubecCMASharp}{47.9} |
| 217 | +\newcommand{\FrozenFirstcondomunicipality}{1.82} | |
| 218 | +\newcommand{\FrozenFirstcondoprovince}{0.16} | |
| 219 | +\newcommand{\FrozenFirstcondoregion}{0.98} | |
| 220 | +\newcommand{\FrozenFirsthousemunicipality}{2.31} | |
| 221 | +\newcommand{\FrozenFirsthouseprovince}{0.16} | |
| 222 | +\newcommand{\FrozenFirsthouseregion}{1.93} | |
| 223 | +\newcommand{\FrozenFirstplexmunicipality}{3.03} | |
| 224 | +\newcommand{\FrozenFirstplexprovince}{0.26} | |
| 225 | +\newcommand{\FrozenFirstplexregion}{1.37} | |
| 226 | +\newcommand{\MCmunicipalityAmedianbaselineBias}{-0.043} | |
| 227 | +\newcommand{\MCmunicipalityAmedianbaselineCov}{n/a} | |
| 228 | +\newcommand{\MCmunicipalityAmedianbaselineLag}{-0.00} | |
| 229 | +\newcommand{\MCmunicipalityAmedianbaselineRev}{n/a} | |
| 230 | +\newcommand{\MCmunicipalityAmedianbaselineRmse}{24.28} | |
| 231 | +\newcommand{\MCmunicipalityAmedianbaselineShare}{82} | |
| 232 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsBias}{-0.040} | |
| 233 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsCov}{n/a} | |
| 234 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsLag}{-0.00} | |
| 235 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsRev}{n/a} | |
| 236 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsRmse}{25.89} | |
| 237 | +\newcommand{\MCmunicipalityAmediandriftinghedonicsShare}{82} | |
| 238 | +\newcommand{\MCmunicipalityAmedianflatqualityupBias}{0.211} | |
| 239 | +\newcommand{\MCmunicipalityAmedianflatqualityupCov}{n/a} | |
| 240 | +\newcommand{\MCmunicipalityAmedianflatqualityupLag}{-0.02} | |
| 241 | +\newcommand{\MCmunicipalityAmedianflatqualityupRev}{n/a} | |
| 242 | +\newcommand{\MCmunicipalityAmedianflatqualityupRmse}{24.71} | |
| 243 | +\newcommand{\MCmunicipalityAmedianflatqualityupShare}{82} | |
| 244 | +\newcommand{\MCmunicipalityAmedianheavytailsBias}{0.093} | |
| 245 | +\newcommand{\MCmunicipalityAmedianheavytailsCov}{n/a} | |
| 246 | +\newcommand{\MCmunicipalityAmedianheavytailsLag}{+0.00} | |
| 247 | +\newcommand{\MCmunicipalityAmedianheavytailsRev}{n/a} | |
| 248 | +\newcommand{\MCmunicipalityAmedianheavytailsRmse}{27.37} | |
| 249 | +\newcommand{\MCmunicipalityAmedianheavytailsShare}{82} | |
| 250 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsBias}{-0.002} | |
| 251 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsCov}{n/a} | |
| 252 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsLag}{+0.01} | |
| 253 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsRev}{n/a} | |
| 254 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsRmse}{24.38} | |
| 255 | +\newcommand{\MCmunicipalityAmedianidiosyncratictownsShare}{82} | |
| 256 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsBias}{-0.043} | |
| 257 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsCov}{n/a} | |
| 258 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsLag}{-0.01} | |
| 259 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsRev}{n/a} | |
| 260 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsRmse}{24.28} | |
| 261 | +\newcommand{\MCmunicipalityAmediannolocaldynamicsShare}{82} | |
| 262 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownBias}{-0.282} | |
| 263 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownCov}{n/a} | |
| 264 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownLag}{-0.00} | |
| 265 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownRev}{n/a} | |
| 266 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownRmse}{23.95} | |
| 267 | +\newcommand{\MCmunicipalityAmedianrisingqualitydownShare}{82} | |
| 268 | +\newcommand{\MCmunicipalityAmediansparseBias}{0.049} | |
| 269 | +\newcommand{\MCmunicipalityAmediansparseCov}{n/a} | |
| 270 | +\newcommand{\MCmunicipalityAmediansparseLag}{-0.01} | |
| 271 | +\newcommand{\MCmunicipalityAmediansparseRev}{n/a} | |
| 272 | +\newcommand{\MCmunicipalityAmediansparseRmse}{29.43} | |
| 273 | +\newcommand{\MCmunicipalityAmediansparseShare}{62} | |
| 274 | +\newcommand{\MCmunicipalityAmedianturningpointBias}{-0.043} | |
| 275 | +\newcommand{\MCmunicipalityAmedianturningpointCov}{n/a} | |
| 276 | +\newcommand{\MCmunicipalityAmedianturningpointLag}{-0.01} | |
| 277 | +\newcommand{\MCmunicipalityAmedianturningpointRev}{n/a} | |
| 278 | +\newcommand{\MCmunicipalityAmedianturningpointRmse}{24.28} | |
| 279 | +\newcommand{\MCmunicipalityAmedianturningpointShare}{82} | |
| 280 | +\newcommand{\MCmunicipalityBstratifiedbaselineBias}{-0.010} | |
| 281 | +\newcommand{\MCmunicipalityBstratifiedbaselineCov}{n/a} | |
| 282 | +\newcommand{\MCmunicipalityBstratifiedbaselineLag}{+0.42} | |
| 283 | +\newcommand{\MCmunicipalityBstratifiedbaselineRev}{n/a} | |
| 284 | +\newcommand{\MCmunicipalityBstratifiedbaselineRmse}{6.01} | |
| 285 | +\newcommand{\MCmunicipalityBstratifiedbaselineShare}{97} | |
| 286 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsBias}{-0.012} | |
| 287 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsCov}{n/a} | |
| 288 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsLag}{+0.41} | |
| 289 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsRev}{n/a} | |
| 290 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsRmse}{6.25} | |
| 291 | +\newcommand{\MCmunicipalityBstratifieddriftinghedonicsShare}{97} | |
| 292 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupBias}{0.136} | |
| 293 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupCov}{n/a} | |
| 294 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupLag}{+0.28} | |
| 295 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupRev}{n/a} | |
| 296 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupRmse}{6.00} | |
| 297 | +\newcommand{\MCmunicipalityBstratifiedflatqualityupShare}{97} | |
| 298 | +\newcommand{\MCmunicipalityBstratifiedheavytailsBias}{-0.021} | |
| 299 | +\newcommand{\MCmunicipalityBstratifiedheavytailsCov}{n/a} | |
| 300 | +\newcommand{\MCmunicipalityBstratifiedheavytailsLag}{+0.32} | |
| 301 | +\newcommand{\MCmunicipalityBstratifiedheavytailsRev}{n/a} | |
| 302 | +\newcommand{\MCmunicipalityBstratifiedheavytailsRmse}{7.60} | |
| 303 | +\newcommand{\MCmunicipalityBstratifiedheavytailsShare}{97} | |
| 304 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsBias}{-0.015} | |
| 305 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsCov}{n/a} | |
| 306 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsLag}{+0.54} | |
| 307 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsRev}{n/a} | |
| 308 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsRmse}{6.16} | |
| 309 | +\newcommand{\MCmunicipalityBstratifiedidiosyncratictownsShare}{97} | |
| 310 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsBias}{-0.010} | |
| 311 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsCov}{n/a} | |
| 312 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsLag}{+0.34} | |
| 313 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsRev}{n/a} | |
| 314 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsRmse}{5.97} | |
| 315 | +\newcommand{\MCmunicipalityBstratifiednolocaldynamicsShare}{97} | |
| 316 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownBias}{-0.153} | |
| 317 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownCov}{n/a} | |
| 318 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownLag}{+0.41} | |
| 319 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownRev}{n/a} | |
| 320 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownRmse}{6.00} | |
| 321 | +\newcommand{\MCmunicipalityBstratifiedrisingqualitydownShare}{97} | |
| 322 | +\newcommand{\MCmunicipalityBstratifiedsparseBias}{-0.019} | |
| 323 | +\newcommand{\MCmunicipalityBstratifiedsparseCov}{n/a} | |
| 324 | +\newcommand{\MCmunicipalityBstratifiedsparseLag}{+0.48} | |
| 325 | +\newcommand{\MCmunicipalityBstratifiedsparseRev}{n/a} | |
| 326 | +\newcommand{\MCmunicipalityBstratifiedsparseRmse}{5.57} | |
| 327 | +\newcommand{\MCmunicipalityBstratifiedsparseShare}{96} | |
| 328 | +\newcommand{\MCmunicipalityBstratifiedturningpointBias}{0.051} | |
| 329 | +\newcommand{\MCmunicipalityBstratifiedturningpointCov}{n/a} | |
| 330 | +\newcommand{\MCmunicipalityBstratifiedturningpointLag}{+0.46} | |
| 331 | +\newcommand{\MCmunicipalityBstratifiedturningpointRev}{n/a} | |
| 332 | +\newcommand{\MCmunicipalityBstratifiedturningpointRmse}{6.02} | |
| 333 | +\newcommand{\MCmunicipalityBstratifiedturningpointShare}{97} | |
| 334 | +\newcommand{\MCmunicipalityCpooledtdbaselineBias}{-0.018} | |
| 335 | +\newcommand{\MCmunicipalityCpooledtdbaselineCov}{97} | |
| 336 | +\newcommand{\MCmunicipalityCpooledtdbaselineLag}{+0.01} | |
| 337 | +\newcommand{\MCmunicipalityCpooledtdbaselineRev}{n/a} | |
| 338 | +\newcommand{\MCmunicipalityCpooledtdbaselineRmse}{12.52} | |
| 339 | +\newcommand{\MCmunicipalityCpooledtdbaselineShare}{96} | |
| 340 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsBias}{-0.019} | |
| 341 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsCov}{97} | |
| 342 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsLag}{+0.03} | |
| 343 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsRev}{n/a} | |
| 344 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsRmse}{13.01} | |
| 345 | +\newcommand{\MCmunicipalityCpooledtddriftinghedonicsShare}{96} | |
| 346 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupBias}{0.029} | |
| 347 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupCov}{97} | |
| 348 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupLag}{+0.00} | |
| 349 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupRev}{n/a} | |
| 350 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupRmse}{12.55} | |
| 351 | +\newcommand{\MCmunicipalityCpooledtdflatqualityupShare}{96} | |
| 352 | +\newcommand{\MCmunicipalityCpooledtdheavytailsBias}{-0.016} | |
| 353 | +\newcommand{\MCmunicipalityCpooledtdheavytailsCov}{97} | |
| 354 | +\newcommand{\MCmunicipalityCpooledtdheavytailsLag}{+0.01} | |
| 355 | +\newcommand{\MCmunicipalityCpooledtdheavytailsRev}{n/a} | |
| 356 | +\newcommand{\MCmunicipalityCpooledtdheavytailsRmse}{15.15} | |
| 357 | +\newcommand{\MCmunicipalityCpooledtdheavytailsShare}{96} | |
| 358 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsBias}{-0.005} | |
| 359 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsCov}{97} | |
| 360 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsLag}{-0.00} | |
| 361 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsRev}{n/a} | |
| 362 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsRmse}{12.57} | |
| 363 | +\newcommand{\MCmunicipalityCpooledtdidiosyncratictownsShare}{96} | |
| 364 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsBias}{-0.018} | |
| 365 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsCov}{97} | |
| 366 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsLag}{+0.02} | |
| 367 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsRev}{n/a} | |
| 368 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsRmse}{12.52} | |
| 369 | +\newcommand{\MCmunicipalityCpooledtdnolocaldynamicsShare}{96} | |
| 370 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownBias}{-0.064} | |
| 371 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownCov}{97} | |
| 372 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownLag}{+0.01} | |
| 373 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownRev}{n/a} | |
| 374 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownRmse}{12.54} | |
| 375 | +\newcommand{\MCmunicipalityCpooledtdrisingqualitydownShare}{96} | |
| 376 | +\newcommand{\MCmunicipalityCpooledtdsparseBias}{-0.012} | |
| 377 | +\newcommand{\MCmunicipalityCpooledtdsparseCov}{96} | |
| 378 | +\newcommand{\MCmunicipalityCpooledtdsparseLag}{-0.05} | |
| 379 | +\newcommand{\MCmunicipalityCpooledtdsparseRev}{n/a} | |
| 380 | +\newcommand{\MCmunicipalityCpooledtdsparseRmse}{15.24} | |
| 381 | +\newcommand{\MCmunicipalityCpooledtdsparseShare}{93} | |
| 382 | +\newcommand{\MCmunicipalityCpooledtdturningpointBias}{-0.018} | |
| 383 | +\newcommand{\MCmunicipalityCpooledtdturningpointCov}{97} | |
| 384 | +\newcommand{\MCmunicipalityCpooledtdturningpointLag}{+0.03} | |
| 385 | +\newcommand{\MCmunicipalityCpooledtdturningpointRev}{n/a} | |
| 386 | +\newcommand{\MCmunicipalityCpooledtdturningpointRmse}{12.52} | |
| 387 | +\newcommand{\MCmunicipalityCpooledtdturningpointShare}{96} | |
| 388 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineBias}{0.002} | |
| 389 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineCov}{n/a} | |
| 390 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineLag}{+0.00} | |
| 391 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineRev}{n/a} | |
| 392 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineRmse}{7.48} | |
| 393 | +\newcommand{\MCmunicipalityDrtdOneThreebaselineShare}{100} | |
| 394 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsBias}{0.002} | |
| 395 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsCov}{n/a} | |
| 396 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsLag}{+0.03} | |
| 397 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsRev}{n/a} | |
| 398 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsRmse}{7.75} | |
| 399 | +\newcommand{\MCmunicipalityDrtdOneThreedriftinghedonicsShare}{100} | |
| 400 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupBias}{0.048} | |
| 401 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupCov}{n/a} | |
| 402 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupLag}{+0.02} | |
| 403 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupRev}{n/a} | |
| 404 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupRmse}{7.50} | |
| 405 | +\newcommand{\MCmunicipalityDrtdOneThreeflatqualityupShare}{100} | |
| 406 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsBias}{0.004} | |
| 407 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsCov}{n/a} | |
| 408 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsLag}{+0.08} | |
| 409 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsRev}{n/a} | |
| 410 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsRmse}{8.14} | |
| 411 | +\newcommand{\MCmunicipalityDrtdOneThreeheavytailsShare}{100} | |
| 412 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsBias}{-0.002} | |
| 413 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsCov}{n/a} | |
| 414 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsLag}{+0.05} | |
| 415 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsRev}{n/a} | |
| 416 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsRmse}{7.48} | |
| 417 | +\newcommand{\MCmunicipalityDrtdOneThreeidiosyncratictownsShare}{100} | |
| 418 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsBias}{0.002} | |
| 419 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsCov}{n/a} | |
| 420 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsLag}{+0.03} | |
| 421 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsRev}{n/a} | |
| 422 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsRmse}{7.48} | |
| 423 | +\newcommand{\MCmunicipalityDrtdOneThreenolocaldynamicsShare}{100} | |
| 424 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownBias}{-0.042} | |
| 425 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownCov}{n/a} | |
| 426 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownLag}{-0.01} | |
| 427 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownRev}{n/a} | |
| 428 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownRmse}{7.48} | |
| 429 | +\newcommand{\MCmunicipalityDrtdOneThreerisingqualitydownShare}{100} | |
| 430 | +\newcommand{\MCmunicipalityDrtdOneThreesparseBias}{0.006} | |
| 431 | +\newcommand{\MCmunicipalityDrtdOneThreesparseCov}{n/a} | |
| 432 | +\newcommand{\MCmunicipalityDrtdOneThreesparseLag}{-0.07} | |
| 433 | +\newcommand{\MCmunicipalityDrtdOneThreesparseRev}{n/a} | |
| 434 | +\newcommand{\MCmunicipalityDrtdOneThreesparseRmse}{7.84} | |
| 435 | +\newcommand{\MCmunicipalityDrtdOneThreesparseShare}{100} | |
| 436 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointBias}{0.002} | |
| 437 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointCov}{n/a} | |
| 438 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointLag}{-0.01} | |
| 439 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointRev}{n/a} | |
| 440 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointRmse}{7.48} | |
| 441 | +\newcommand{\MCmunicipalityDrtdOneThreeturningpointShare}{100} | |
| 442 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineBias}{0.011} | |
| 443 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineCov}{n/a} | |
| 444 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineLag}{-0.10} | |
| 445 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineRev}{n/a} | |
| 446 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineRmse}{39.26} | |
| 447 | +\newcommand{\MCmunicipalityHrepeatsalesbaselineShare}{30} | |
| 448 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsBias}{0.012} | |
| 449 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsCov}{n/a} | |
| 450 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsLag}{-0.06} | |
| 451 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsRev}{n/a} | |
| 452 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsRmse}{39.49} | |
| 453 | +\newcommand{\MCmunicipalityHrepeatsalesdriftinghedonicsShare}{30} | |
| 454 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupBias}{0.120} | |
| 455 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupCov}{n/a} | |
| 456 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupLag}{-0.05} | |
| 457 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupRev}{n/a} | |
| 458 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupRmse}{39.08} | |
| 459 | +\newcommand{\MCmunicipalityHrepeatsalesflatqualityupShare}{30} | |
| 460 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsBias}{-0.218} | |
| 461 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsCov}{n/a} | |
| 462 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsLag}{+0.02} | |
| 463 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsRev}{n/a} | |
| 464 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsRmse}{51.70} | |
| 465 | +\newcommand{\MCmunicipalityHrepeatsalesheavytailsShare}{30} | |
| 466 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsBias}{-0.125} | |
| 467 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsCov}{n/a} | |
| 468 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsLag}{+0.04} | |
| 469 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsRev}{n/a} | |
| 470 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsRmse}{39.17} | |
| 471 | +\newcommand{\MCmunicipalityHrepeatsalesidiosyncratictownsShare}{30} | |
| 472 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsBias}{0.007} | |
| 473 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsCov}{n/a} | |
| 474 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsLag}{-0.04} | |
| 475 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsRev}{n/a} | |
| 476 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsRmse}{39.25} | |
| 477 | +\newcommand{\MCmunicipalityHrepeatsalesnolocaldynamicsShare}{30} | |
| 478 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownBias}{-0.083} | |
| 479 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownCov}{n/a} | |
| 480 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownLag}{-0.11} | |
| 481 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownRev}{n/a} | |
| 482 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownRmse}{39.80} | |
| 483 | +\newcommand{\MCmunicipalityHrepeatsalesrisingqualitydownShare}{30} | |
| 484 | +\newcommand{\MCmunicipalityHrepeatsalessparseBias}{-0.190} | |
| 485 | +\newcommand{\MCmunicipalityHrepeatsalessparseCov}{n/a} | |
| 486 | +\newcommand{\MCmunicipalityHrepeatsalessparseLag}{+0.01} | |
| 487 | +\newcommand{\MCmunicipalityHrepeatsalessparseRev}{n/a} | |
| 488 | +\newcommand{\MCmunicipalityHrepeatsalessparseRmse}{42.97} | |
| 489 | +\newcommand{\MCmunicipalityHrepeatsalessparseShare}{10} | |
| 490 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointBias}{0.034} | |
| 491 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointCov}{n/a} | |
| 492 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointLag}{-0.04} | |
| 493 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointRev}{n/a} | |
| 494 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointRmse}{39.23} | |
| 495 | +\newcommand{\MCmunicipalityHrepeatsalesturningpointShare}{30} | |
| 496 | +\newcommand{\MCmunicipalityKqhpibaselineBias}{-0.001} | |
| 497 | +\newcommand{\MCmunicipalityKqhpibaselineCov}{89} | |
| 498 | +\newcommand{\MCmunicipalityKqhpibaselineLag}{+0.01} | |
| 499 | +\newcommand{\MCmunicipalityKqhpibaselineRev}{n/a} | |
| 500 | +\newcommand{\MCmunicipalityKqhpibaselineRmse}{2.91} | |
| 501 | +\newcommand{\MCmunicipalityKqhpibaselineShare}{100} | |
| 502 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsBias}{0.001} | |
| 503 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsCov}{89} | |
| 504 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsLag}{+0.01} | |
| 505 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsRev}{n/a} | |
| 506 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsRmse}{2.93} | |
| 507 | +\newcommand{\MCmunicipalityKqhpidriftinghedonicsShare}{100} | |
| 508 | +\newcommand{\MCmunicipalityKqhpiflatqualityupBias}{0.046} | |
| 509 | +\newcommand{\MCmunicipalityKqhpiflatqualityupCov}{87} | |
| 510 | +\newcommand{\MCmunicipalityKqhpiflatqualityupLag}{+0.03} | |
| 511 | +\newcommand{\MCmunicipalityKqhpiflatqualityupRev}{n/a} | |
| 512 | +\newcommand{\MCmunicipalityKqhpiflatqualityupRmse}{2.90} | |
| 513 | +\newcommand{\MCmunicipalityKqhpiflatqualityupShare}{100} | |
| 514 | +\newcommand{\MCmunicipalityKqhpiheavytailsBias}{-0.005} | |
| 515 | +\newcommand{\MCmunicipalityKqhpiheavytailsCov}{83} | |
| 516 | +\newcommand{\MCmunicipalityKqhpiheavytailsLag}{-0.00} | |
| 517 | +\newcommand{\MCmunicipalityKqhpiheavytailsRev}{n/a} | |
| 518 | +\newcommand{\MCmunicipalityKqhpiheavytailsRmse}{7.89} | |
| 519 | +\newcommand{\MCmunicipalityKqhpiheavytailsShare}{100} | |
| 520 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsBias}{-0.010} | |
| 521 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsCov}{89} | |
| 522 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsLag}{-0.00} | |
| 523 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsRev}{n/a} | |
| 524 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsRmse}{3.30} | |
| 525 | +\newcommand{\MCmunicipalityKqhpiidiosyncratictownsShare}{100} | |
| 526 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsBias}{-0.001} | |
| 527 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsCov}{90} | |
| 528 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsLag}{-0.00} | |
| 529 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsRev}{n/a} | |
| 530 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsRmse}{2.84} | |
| 531 | +\newcommand{\MCmunicipalityKqhpinolocaldynamicsShare}{100} | |
| 532 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownBias}{-0.045} | |
| 533 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownCov}{87} | |
| 534 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownLag}{+0.02} | |
| 535 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownRev}{n/a} | |
| 536 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownRmse}{2.91} | |
| 537 | +\newcommand{\MCmunicipalityKqhpirisingqualitydownShare}{100} | |
| 538 | +\newcommand{\MCmunicipalityKqhpisparseBias}{-0.005} | |
| 539 | +\newcommand{\MCmunicipalityKqhpisparseCov}{89} | |
| 540 | +\newcommand{\MCmunicipalityKqhpisparseLag}{+0.01} | |
| 541 | +\newcommand{\MCmunicipalityKqhpisparseRev}{n/a} | |
| 542 | +\newcommand{\MCmunicipalityKqhpisparseRmse}{3.57} | |
| 543 | +\newcommand{\MCmunicipalityKqhpisparseShare}{100} | |
| 544 | +\newcommand{\MCmunicipalityKqhpiturningpointBias}{-0.000} | |
| 545 | +\newcommand{\MCmunicipalityKqhpiturningpointCov}{89} | |
| 546 | +\newcommand{\MCmunicipalityKqhpiturningpointLag}{+0.02} | |
| 547 | +\newcommand{\MCmunicipalityKqhpiturningpointRev}{n/a} | |
| 548 | +\newcommand{\MCmunicipalityKqhpiturningpointRmse}{2.91} | |
| 549 | +\newcommand{\MCmunicipalityKqhpiturningpointShare}{100} | |
| 550 | +\newcommand{\MCmunicipalitySLLTbaselineBias}{0.003} | |
| 551 | +\newcommand{\MCmunicipalitySLLTbaselineCov}{97} | |
| 552 | +\newcommand{\MCmunicipalitySLLTbaselineLag}{+0.01} | |
| 553 | +\newcommand{\MCmunicipalitySLLTbaselineRev}{4.57} | |
| 554 | +\newcommand{\MCmunicipalitySLLTbaselineRmse}{1.61} | |
| 555 | +\newcommand{\MCmunicipalitySLLTbaselineShare}{100} | |
| 556 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsBias}{0.003} | |
| 557 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsCov}{97} | |
| 558 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsLag}{+0.01} | |
| 559 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsRev}{4.61} | |
| 560 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsRmse}{1.65} | |
| 561 | +\newcommand{\MCmunicipalitySLLTdriftinghedonicsShare}{100} | |
| 562 | +\newcommand{\MCmunicipalitySLLTflatqualityupBias}{0.053} | |
| 563 | +\newcommand{\MCmunicipalitySLLTflatqualityupCov}{96} | |
| 564 | +\newcommand{\MCmunicipalitySLLTflatqualityupLag}{+0.02} | |
| 565 | +\newcommand{\MCmunicipalitySLLTflatqualityupRev}{4.57} | |
| 566 | +\newcommand{\MCmunicipalitySLLTflatqualityupRmse}{1.61} | |
| 567 | +\newcommand{\MCmunicipalitySLLTflatqualityupShare}{100} | |
| 568 | +\newcommand{\MCmunicipalitySLLTheavytailsBias}{-0.005} | |
| 569 | +\newcommand{\MCmunicipalitySLLTheavytailsCov}{90} | |
| 570 | +\newcommand{\MCmunicipalitySLLTheavytailsLag}{-0.01} | |
| 571 | +\newcommand{\MCmunicipalitySLLTheavytailsRev}{7.17} | |
| 572 | +\newcommand{\MCmunicipalitySLLTheavytailsRmse}{4.95} | |
| 573 | +\newcommand{\MCmunicipalitySLLTheavytailsShare}{100} | |
| 574 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsBias}{-0.010} | |
| 575 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsCov}{93} | |
| 576 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsLag}{+0.03} | |
| 577 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsRev}{5.03} | |
| 578 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsRmse}{2.51} | |
| 579 | +\newcommand{\MCmunicipalitySLLTidiosyncratictownsShare}{100} | |
| 580 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsBias}{0.002} | |
| 581 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsCov}{98} | |
| 582 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsLag}{+0.00} | |
| 583 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsRev}{4.52} | |
| 584 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsRmse}{1.40} | |
| 585 | +\newcommand{\MCmunicipalitySLLTnolocaldynamicsShare}{100} | |
| 586 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownBias}{-0.042} | |
| 587 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownCov}{96} | |
| 588 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownLag}{+0.01} | |
| 589 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownRev}{4.57} | |
| 590 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownRmse}{1.61} | |
| 591 | +\newcommand{\MCmunicipalitySLLTrisingqualitydownShare}{100} | |
| 592 | +\newcommand{\MCmunicipalitySLLTsparseBias}{0.006} | |
| 593 | +\newcommand{\MCmunicipalitySLLTsparseCov}{97} | |
| 594 | +\newcommand{\MCmunicipalitySLLTsparseLag}{+0.02} | |
| 595 | +\newcommand{\MCmunicipalitySLLTsparseRev}{6.42} | |
| 596 | +\newcommand{\MCmunicipalitySLLTsparseRmse}{2.45} | |
| 597 | +\newcommand{\MCmunicipalitySLLTsparseShare}{100} | |
| 598 | +\newcommand{\MCmunicipalitySLLTturningpointBias}{0.003} | |
| 599 | +\newcommand{\MCmunicipalitySLLTturningpointCov}{97} | |
| 600 | +\newcommand{\MCmunicipalitySLLTturningpointLag}{+0.01} | |
| 601 | +\newcommand{\MCmunicipalitySLLTturningpointRev}{4.57} | |
| 602 | +\newcommand{\MCmunicipalitySLLTturningpointRmse}{1.61} | |
| 603 | +\newcommand{\MCmunicipalitySLLTturningpointShare}{100} | |
| 604 | +\newcommand{\MCmunicipalityScoarselocbaselineBias}{0.011} | |
| 605 | +\newcommand{\MCmunicipalityScoarselocbaselineCov}{99} | |
| 606 | +\newcommand{\MCmunicipalityScoarselocbaselineLag}{+0.01} | |
| 607 | +\newcommand{\MCmunicipalityScoarselocbaselineRev}{2.67} | |
| 608 | +\newcommand{\MCmunicipalityScoarselocbaselineRmse}{2.05} | |
| 609 | +\newcommand{\MCmunicipalityScoarselocbaselineShare}{100} | |
| 610 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsBias}{0.011} | |
| 611 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsCov}{99} | |
| 612 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsLag}{+0.02} | |
| 613 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsRev}{2.68} | |
| 614 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsRmse}{2.08} | |
| 615 | +\newcommand{\MCmunicipalityScoarselocdriftinghedonicsShare}{100} | |
| 616 | +\newcommand{\MCmunicipalityScoarselocflatqualityupBias}{0.046} | |
| 617 | +\newcommand{\MCmunicipalityScoarselocflatqualityupCov}{99} | |
| 618 | +\newcommand{\MCmunicipalityScoarselocflatqualityupLag}{+0.06} | |
| 619 | +\newcommand{\MCmunicipalityScoarselocflatqualityupRev}{2.67} | |
| 620 | +\newcommand{\MCmunicipalityScoarselocflatqualityupRmse}{2.05} | |
| 621 | +\newcommand{\MCmunicipalityScoarselocflatqualityupShare}{100} | |
| 622 | +\newcommand{\MCmunicipalityScoarselocheavytailsBias}{-0.001} | |
| 623 | +\newcommand{\MCmunicipalityScoarselocheavytailsCov}{98} | |
| 624 | +\newcommand{\MCmunicipalityScoarselocheavytailsLag}{+0.01} | |
| 625 | +\newcommand{\MCmunicipalityScoarselocheavytailsRev}{3.89} | |
| 626 | +\newcommand{\MCmunicipalityScoarselocheavytailsRmse}{2.84} | |
| 627 | +\newcommand{\MCmunicipalityScoarselocheavytailsShare}{100} | |
| 628 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsBias}{-0.005} | |
| 629 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsCov}{99} | |
| 630 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsLag}{+0.03} | |
| 631 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsRev}{3.25} | |
| 632 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsRmse}{2.81} | |
| 633 | +\newcommand{\MCmunicipalityScoarselocidiosyncratictownsShare}{100} | |
| 634 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsBias}{0.008} | |
| 635 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsCov}{100} | |
| 636 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsLag}{+0.01} | |
| 637 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsRev}{2.52} | |
| 638 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsRmse}{1.88} | |
| 639 | +\newcommand{\MCmunicipalityScoarselocnolocaldynamicsShare}{100} | |
| 640 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownBias}{-0.022} | |
| 641 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownCov}{99} | |
| 642 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownLag}{+0.01} | |
| 643 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownRev}{2.67} | |
| 644 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownRmse}{2.05} | |
| 645 | +\newcommand{\MCmunicipalityScoarselocrisingqualitydownShare}{100} | |
| 646 | +\newcommand{\MCmunicipalityScoarselocsparseBias}{-0.006} | |
| 647 | +\newcommand{\MCmunicipalityScoarselocsparseCov}{99} | |
| 648 | +\newcommand{\MCmunicipalityScoarselocsparseLag}{+0.09} | |
| 649 | +\newcommand{\MCmunicipalityScoarselocsparseRev}{3.68} | |
| 650 | +\newcommand{\MCmunicipalityScoarselocsparseRmse}{3.30} | |
| 651 | +\newcommand{\MCmunicipalityScoarselocsparseShare}{100} | |
| 652 | +\newcommand{\MCmunicipalityScoarselocturningpointBias}{0.011} | |
| 653 | +\newcommand{\MCmunicipalityScoarselocturningpointCov}{99} | |
| 654 | +\newcommand{\MCmunicipalityScoarselocturningpointLag}{+0.01} | |
| 655 | +\newcommand{\MCmunicipalityScoarselocturningpointRev}{2.67} | |
| 656 | +\newcommand{\MCmunicipalityScoarselocturningpointRmse}{2.05} | |
| 657 | +\newcommand{\MCmunicipalityScoarselocturningpointShare}{100} | |
| 658 | +\newcommand{\MCmunicipalitySnoEBbaselineBias}{0.003} | |
| 659 | +\newcommand{\MCmunicipalitySnoEBbaselineCov}{97} | |
| 660 | +\newcommand{\MCmunicipalitySnoEBbaselineLag}{+0.01} | |
| 661 | +\newcommand{\MCmunicipalitySnoEBbaselineRev}{2.37} | |
| 662 | +\newcommand{\MCmunicipalitySnoEBbaselineRmse}{1.62} | |
| 663 | +\newcommand{\MCmunicipalitySnoEBbaselineShare}{100} | |
| 664 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsBias}{0.003} | |
| 665 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsCov}{98} | |
| 666 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsLag}{+0.01} | |
| 667 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsRev}{2.40} | |
| 668 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsRmse}{1.66} | |
| 669 | +\newcommand{\MCmunicipalitySnoEBdriftinghedonicsShare}{100} | |
| 670 | +\newcommand{\MCmunicipalitySnoEBflatqualityupBias}{0.052} | |
| 671 | +\newcommand{\MCmunicipalitySnoEBflatqualityupCov}{96} | |
| 672 | +\newcommand{\MCmunicipalitySnoEBflatqualityupLag}{+0.02} | |
| 673 | +\newcommand{\MCmunicipalitySnoEBflatqualityupRev}{2.38} | |
| 674 | +\newcommand{\MCmunicipalitySnoEBflatqualityupRmse}{1.62} | |
| 675 | +\newcommand{\MCmunicipalitySnoEBflatqualityupShare}{100} | |
| 676 | +\newcommand{\MCmunicipalitySnoEBheavytailsBias}{-0.007} | |
| 677 | +\newcommand{\MCmunicipalitySnoEBheavytailsCov}{91} | |
| 678 | +\newcommand{\MCmunicipalitySnoEBheavytailsLag}{-0.02} | |
| 679 | +\newcommand{\MCmunicipalitySnoEBheavytailsRev}{4.30} | |
| 680 | +\newcommand{\MCmunicipalitySnoEBheavytailsRmse}{4.51} | |
| 681 | +\newcommand{\MCmunicipalitySnoEBheavytailsShare}{100} | |
| 682 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsBias}{-0.005} | |
| 683 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsCov}{93} | |
| 684 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsLag}{+0.02} | |
| 685 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsRev}{2.99} | |
| 686 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsRmse}{2.55} | |
| 687 | +\newcommand{\MCmunicipalitySnoEBidiosyncratictownsShare}{100} | |
| 688 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsBias}{0.002} | |
| 689 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsCov}{99} | |
| 690 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsLag}{+0.00} | |
| 691 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsRev}{2.25} | |
| 692 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsRmse}{1.40} | |
| 693 | +\newcommand{\MCmunicipalitySnoEBnolocaldynamicsShare}{100} | |
| 694 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownBias}{-0.043} | |
| 695 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownCov}{96} | |
| 696 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownLag}{+0.01} | |
| 697 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownRev}{2.38} | |
| 698 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownRmse}{1.62} | |
| 699 | +\newcommand{\MCmunicipalitySnoEBrisingqualitydownShare}{100} | |
| 700 | +\newcommand{\MCmunicipalitySnoEBsparseBias}{0.005} | |
| 701 | +\newcommand{\MCmunicipalitySnoEBsparseCov}{98} | |
| 702 | +\newcommand{\MCmunicipalitySnoEBsparseLag}{+0.02} | |
| 703 | +\newcommand{\MCmunicipalitySnoEBsparseRev}{3.22} | |
| 704 | +\newcommand{\MCmunicipalitySnoEBsparseRmse}{2.45} | |
| 705 | +\newcommand{\MCmunicipalitySnoEBsparseShare}{100} | |
| 706 | +\newcommand{\MCmunicipalitySnoEBturningpointBias}{0.003} | |
| 707 | +\newcommand{\MCmunicipalitySnoEBturningpointCov}{97} | |
| 708 | +\newcommand{\MCmunicipalitySnoEBturningpointLag}{+0.01} | |
| 709 | +\newcommand{\MCmunicipalitySnoEBturningpointRev}{2.37} | |
| 710 | +\newcommand{\MCmunicipalitySnoEBturningpointRmse}{1.62} | |
| 711 | +\newcommand{\MCmunicipalitySnoEBturningpointShare}{100} | |
| 712 | +\newcommand{\MCmunicipalitySnoTVbaselineBias}{0.002} | |
| 713 | +\newcommand{\MCmunicipalitySnoTVbaselineCov}{98} | |
| 714 | +\newcommand{\MCmunicipalitySnoTVbaselineLag}{+0.00} | |
| 715 | +\newcommand{\MCmunicipalitySnoTVbaselineRev}{2.37} | |
| 716 | +\newcommand{\MCmunicipalitySnoTVbaselineRmse}{1.58} | |
| 717 | +\newcommand{\MCmunicipalitySnoTVbaselineShare}{100} | |
| 718 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsBias}{0.002} | |
| 719 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsCov}{98} | |
| 720 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsLag}{+0.01} | |
| 721 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsRev}{2.40} | |
| 722 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsRmse}{1.61} | |
| 723 | +\newcommand{\MCmunicipalitySnoTVdriftinghedonicsShare}{100} | |
| 724 | +\newcommand{\MCmunicipalitySnoTVflatqualityupBias}{0.048} | |
| 725 | +\newcommand{\MCmunicipalitySnoTVflatqualityupCov}{98} | |
| 726 | +\newcommand{\MCmunicipalitySnoTVflatqualityupLag}{+0.01} | |
| 727 | +\newcommand{\MCmunicipalitySnoTVflatqualityupRev}{2.37} | |
| 728 | +\newcommand{\MCmunicipalitySnoTVflatqualityupRmse}{1.58} | |
| 729 | +\newcommand{\MCmunicipalitySnoTVflatqualityupShare}{100} | |
| 730 | +\newcommand{\MCmunicipalitySnoTVheavytailsBias}{-0.007} | |
| 731 | +\newcommand{\MCmunicipalitySnoTVheavytailsCov}{92} | |
| 732 | +\newcommand{\MCmunicipalitySnoTVheavytailsLag}{-0.01} | |
| 733 | +\newcommand{\MCmunicipalitySnoTVheavytailsRev}{4.80} | |
| 734 | +\newcommand{\MCmunicipalitySnoTVheavytailsRmse}{4.62} | |
| 735 | +\newcommand{\MCmunicipalitySnoTVheavytailsShare}{100} | |
| 736 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsBias}{-0.006} | |
| 737 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsCov}{96} | |
| 738 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsLag}{+0.02} | |
| 739 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsRev}{3.10} | |
| 740 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsRmse}{2.52} | |
| 741 | +\newcommand{\MCmunicipalitySnoTVidiosyncratictownsShare}{100} | |
| 742 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsBias}{0.001} | |
| 743 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsCov}{99} | |
| 744 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsLag}{+0.00} | |
| 745 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsRev}{2.20} | |
| 746 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsRmse}{1.35} | |
| 747 | +\newcommand{\MCmunicipalitySnoTVnolocaldynamicsShare}{100} | |
| 748 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownBias}{-0.042} | |
| 749 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownCov}{98} | |
| 750 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownLag}{+0.01} | |
| 751 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownRev}{2.37} | |
| 752 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownRmse}{1.58} | |
| 753 | +\newcommand{\MCmunicipalitySnoTVrisingqualitydownShare}{100} | |
| 754 | +\newcommand{\MCmunicipalitySnoTVsparseBias}{0.002} | |
| 755 | +\newcommand{\MCmunicipalitySnoTVsparseCov}{99} | |
| 756 | +\newcommand{\MCmunicipalitySnoTVsparseLag}{+0.03} | |
| 757 | +\newcommand{\MCmunicipalitySnoTVsparseRev}{3.17} | |
| 758 | +\newcommand{\MCmunicipalitySnoTVsparseRmse}{2.40} | |
| 759 | +\newcommand{\MCmunicipalitySnoTVsparseShare}{100} | |
| 760 | +\newcommand{\MCmunicipalitySnoTVturningpointBias}{0.002} | |
| 761 | +\newcommand{\MCmunicipalitySnoTVturningpointCov}{98} | |
| 762 | +\newcommand{\MCmunicipalitySnoTVturningpointLag}{+0.00} | |
| 763 | +\newcommand{\MCmunicipalitySnoTVturningpointRev}{2.37} | |
| 764 | +\newcommand{\MCmunicipalitySnoTVturningpointRmse}{1.58} | |
| 765 | +\newcommand{\MCmunicipalitySnoTVturningpointShare}{100} | |
| 766 | +\newcommand{\MCmunicipalitySnorobustbaselineBias}{0.004} | |
| 767 | +\newcommand{\MCmunicipalitySnorobustbaselineCov}{98} | |
| 768 | +\newcommand{\MCmunicipalitySnorobustbaselineLag}{+0.01} | |
| 769 | +\newcommand{\MCmunicipalitySnorobustbaselineRev}{3.04} | |
| 770 | +\newcommand{\MCmunicipalitySnorobustbaselineRmse}{2.25} | |
| 771 | +\newcommand{\MCmunicipalitySnorobustbaselineShare}{100} | |
| 772 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsBias}{0.004} | |
| 773 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsCov}{98} | |
| 774 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsLag}{+0.00} | |
| 775 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsRev}{3.07} | |
| 776 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsRmse}{2.29} | |
| 777 | +\newcommand{\MCmunicipalitySnorobustdriftinghedonicsShare}{100} | |
| 778 | +\newcommand{\MCmunicipalitySnorobustflatqualityupBias}{0.053} | |
| 779 | +\newcommand{\MCmunicipalitySnorobustflatqualityupCov}{97} | |
| 780 | +\newcommand{\MCmunicipalitySnorobustflatqualityupLag}{-0.01} | |
| 781 | +\newcommand{\MCmunicipalitySnorobustflatqualityupRev}{3.03} | |
| 782 | +\newcommand{\MCmunicipalitySnorobustflatqualityupRmse}{2.24} | |
| 783 | +\newcommand{\MCmunicipalitySnorobustflatqualityupShare}{100} | |
| 784 | +\newcommand{\MCmunicipalitySnorobustheavytailsBias}{-0.005} | |
| 785 | +\newcommand{\MCmunicipalitySnorobustheavytailsCov}{92} | |
| 786 | +\newcommand{\MCmunicipalitySnorobustheavytailsLag}{-0.01} | |
| 787 | +\newcommand{\MCmunicipalitySnorobustheavytailsRev}{6.16} | |
| 788 | +\newcommand{\MCmunicipalitySnorobustheavytailsRmse}{6.77} | |
| 789 | +\newcommand{\MCmunicipalitySnorobustheavytailsShare}{100} | |
| 790 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsBias}{-0.006} | |
| 791 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsCov}{97} | |
| 792 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsLag}{+0.01} | |
| 793 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsRev}{3.65} | |
| 794 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsRmse}{2.84} | |
| 795 | +\newcommand{\MCmunicipalitySnorobustidiosyncratictownsShare}{100} | |
| 796 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsBias}{0.002} | |
| 797 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsCov}{98} | |
| 798 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsLag}{-0.00} | |
| 799 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsRev}{2.89} | |
| 800 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsRmse}{2.13} | |
| 801 | +\newcommand{\MCmunicipalitySnorobustnolocaldynamicsShare}{100} | |
| 802 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownBias}{-0.042} | |
| 803 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownCov}{97} | |
| 804 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownLag}{-0.00} | |
| 805 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownRev}{3.02} | |
| 806 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownRmse}{2.23} | |
| 807 | +\newcommand{\MCmunicipalitySnorobustrisingqualitydownShare}{100} | |
| 808 | +\newcommand{\MCmunicipalitySnorobustsparseBias}{0.007} | |
| 809 | +\newcommand{\MCmunicipalitySnorobustsparseCov}{98} | |
| 810 | +\newcommand{\MCmunicipalitySnorobustsparseLag}{+0.02} | |
| 811 | +\newcommand{\MCmunicipalitySnorobustsparseRev}{3.95} | |
| 812 | +\newcommand{\MCmunicipalitySnorobustsparseRmse}{3.21} | |
| 813 | +\newcommand{\MCmunicipalitySnorobustsparseShare}{100} | |
| 814 | +\newcommand{\MCmunicipalitySnorobustturningpointBias}{0.004} | |
| 815 | +\newcommand{\MCmunicipalitySnorobustturningpointCov}{98} | |
| 816 | +\newcommand{\MCmunicipalitySnorobustturningpointLag}{+0.00} | |
| 817 | +\newcommand{\MCmunicipalitySnorobustturningpointRev}{3.04} | |
| 818 | +\newcommand{\MCmunicipalitySnorobustturningpointRmse}{2.25} | |
| 819 | +\newcommand{\MCmunicipalitySnorobustturningpointShare}{100} | |
| 820 | +\newcommand{\MCmunicipalitySsharpbaselineBias}{0.003} | |
| 821 | +\newcommand{\MCmunicipalitySsharpbaselineCov}{98} | |
| 822 | +\newcommand{\MCmunicipalitySsharpbaselineLag}{+0.01} | |
| 823 | +\newcommand{\MCmunicipalitySsharpbaselineRev}{2.36} | |
| 824 | +\newcommand{\MCmunicipalitySsharpbaselineRmse}{1.58} | |
| 825 | +\newcommand{\MCmunicipalitySsharpbaselineShare}{100} | |
| 826 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsBias}{0.004} | |
| 827 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsCov}{98} | |
| 828 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsLag}{+0.01} | |
| 829 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsRev}{2.39} | |
| 830 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsRmse}{1.62} | |
| 831 | +\newcommand{\MCmunicipalitySsharpdriftinghedonicsShare}{100} | |
| 832 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineBias}{0.001} | |
| 833 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineCov}{97} | |
| 834 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineLag}{+0.11} | |
| 835 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineRev}{n/a} | |
| 836 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineRmse}{3.02} | |
| 837 | +\newcommand{\MCmunicipalitySsharpfilteredbaselineShare}{100} | |
| 838 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsBias}{0.002} | |
| 839 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsCov}{97} | |
| 840 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsLag}{+0.11} | |
| 841 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsRev}{n/a} | |
| 842 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsRmse}{3.06} | |
| 843 | +\newcommand{\MCmunicipalitySsharpfiltereddriftinghedonicsShare}{100} | |
| 844 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupBias}{0.050} | |
| 845 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupCov}{96} | |
| 846 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupLag}{+0.34} | |
| 847 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupRev}{n/a} | |
| 848 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupRmse}{3.02} | |
| 849 | +\newcommand{\MCmunicipalitySsharpfilteredflatqualityupShare}{100} | |
| 850 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsBias}{-0.012} | |
| 851 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsCov}{89} | |
| 852 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsLag}{+0.06} | |
| 853 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsRev}{n/a} | |
| 854 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsRmse}{8.23} | |
| 855 | +\newcommand{\MCmunicipalitySsharpfilteredheavytailsShare}{100} | |
| 856 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsBias}{0.005} | |
| 857 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsCov}{95} | |
| 858 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsLag}{+0.48} | |
| 859 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsRev}{n/a} | |
| 860 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsRmse}{4.24} | |
| 861 | +\newcommand{\MCmunicipalitySsharpfilteredidiosyncratictownsShare}{100} | |
| 862 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsBias}{0.001} | |
| 863 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsCov}{98} | |
| 864 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsLag}{+0.05} | |
| 865 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsRev}{n/a} | |
| 866 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsRmse}{2.73} | |
| 867 | +\newcommand{\MCmunicipalitySsharpfilterednolocaldynamicsShare}{100} | |
| 868 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownBias}{-0.045} | |
| 869 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownCov}{96} | |
| 870 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownLag}{+0.12} | |
| 871 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownRev}{n/a} | |
| 872 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownRmse}{3.02} | |
| 873 | +\newcommand{\MCmunicipalitySsharpfilteredrisingqualitydownShare}{100} | |
| 874 | +\newcommand{\MCmunicipalitySsharpfilteredsparseBias}{0.008} | |
| 875 | +\newcommand{\MCmunicipalitySsharpfilteredsparseCov}{98} | |
| 876 | +\newcommand{\MCmunicipalitySsharpfilteredsparseLag}{+0.19} | |
| 877 | +\newcommand{\MCmunicipalitySsharpfilteredsparseRev}{n/a} | |
| 878 | +\newcommand{\MCmunicipalitySsharpfilteredsparseRmse}{4.17} | |
| 879 | +\newcommand{\MCmunicipalitySsharpfilteredsparseShare}{100} | |
| 880 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointBias}{0.001} | |
| 881 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointCov}{97} | |
| 882 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointLag}{+0.20} | |
| 883 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointRev}{n/a} | |
| 884 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointRmse}{3.02} | |
| 885 | +\newcommand{\MCmunicipalitySsharpfilteredturningpointShare}{100} | |
| 886 | +\newcommand{\MCmunicipalitySsharpflatqualityupBias}{0.052} | |
| 887 | +\newcommand{\MCmunicipalitySsharpflatqualityupCov}{97} | |
| 888 | +\newcommand{\MCmunicipalitySsharpflatqualityupLag}{+0.01} | |
| 889 | +\newcommand{\MCmunicipalitySsharpflatqualityupRev}{2.37} | |
| 890 | +\newcommand{\MCmunicipalitySsharpflatqualityupRmse}{1.58} | |
| 891 | +\newcommand{\MCmunicipalitySsharpflatqualityupShare}{100} | |
| 892 | +\newcommand{\MCmunicipalitySsharpheavytailsBias}{-0.005} | |
| 893 | +\newcommand{\MCmunicipalitySsharpheavytailsCov}{92} | |
| 894 | +\newcommand{\MCmunicipalitySsharpheavytailsLag}{-0.01} | |
| 895 | +\newcommand{\MCmunicipalitySsharpheavytailsRev}{4.80} | |
| 896 | +\newcommand{\MCmunicipalitySsharpheavytailsRmse}{4.62} | |
| 897 | +\newcommand{\MCmunicipalitySsharpheavytailsShare}{100} | |
| 898 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsBias}{-0.005} | |
| 899 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsCov}{96} | |
| 900 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsLag}{+0.02} | |
| 901 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsRev}{3.10} | |
| 902 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsRmse}{2.52} | |
| 903 | +\newcommand{\MCmunicipalitySsharpidiosyncratictownsShare}{100} | |
| 904 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsBias}{0.002} | |
| 905 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsCov}{99} | |
| 906 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsLag}{+0.00} | |
| 907 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsRev}{2.20} | |
| 908 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsRmse}{1.35} | |
| 909 | +\newcommand{\MCmunicipalitySsharpnolocaldynamicsShare}{100} | |
| 910 | +\newcommand{\MCmunicipalitySsharprisingqualitydownBias}{-0.043} | |
| 911 | +\newcommand{\MCmunicipalitySsharprisingqualitydownCov}{98} | |
| 912 | +\newcommand{\MCmunicipalitySsharprisingqualitydownLag}{+0.01} | |
| 913 | +\newcommand{\MCmunicipalitySsharprisingqualitydownRev}{2.37} | |
| 914 | +\newcommand{\MCmunicipalitySsharprisingqualitydownRmse}{1.58} | |
| 915 | +\newcommand{\MCmunicipalitySsharprisingqualitydownShare}{100} | |
| 916 | +\newcommand{\MCmunicipalitySsharpsparseBias}{0.004} | |
| 917 | +\newcommand{\MCmunicipalitySsharpsparseCov}{99} | |
| 918 | +\newcommand{\MCmunicipalitySsharpsparseLag}{+0.02} | |
| 919 | +\newcommand{\MCmunicipalitySsharpsparseRev}{3.16} | |
| 920 | +\newcommand{\MCmunicipalitySsharpsparseRmse}{2.40} | |
| 921 | +\newcommand{\MCmunicipalitySsharpsparseShare}{100} | |
| 922 | +\newcommand{\MCmunicipalitySsharpturningpointBias}{0.003} | |
| 923 | +\newcommand{\MCmunicipalitySsharpturningpointCov}{98} | |
| 924 | +\newcommand{\MCmunicipalitySsharpturningpointLag}{+0.00} | |
| 925 | +\newcommand{\MCmunicipalitySsharpturningpointRev}{2.36} | |
| 926 | +\newcommand{\MCmunicipalitySsharpturningpointRmse}{1.58} | |
| 927 | +\newcommand{\MCmunicipalitySsharpturningpointShare}{100} | |
| 928 | +\newcommand{\MCprovinceAmedianbaselineBias}{0.010} | |
| 929 | +\newcommand{\MCprovinceAmedianbaselineCov}{n/a} | |
| 930 | +\newcommand{\MCprovinceAmedianbaselineLag}{+0.14} | |
| 931 | +\newcommand{\MCprovinceAmedianbaselineRev}{n/a} | |
| 932 | +\newcommand{\MCprovinceAmedianbaselineRmse}{3.56} | |
| 933 | +\newcommand{\MCprovinceAmedianbaselineShare}{100} | |
| 934 | +\newcommand{\MCprovinceAmediandriftinghedonicsBias}{0.010} | |
| 935 | +\newcommand{\MCprovinceAmediandriftinghedonicsCov}{n/a} | |
| 936 | +\newcommand{\MCprovinceAmediandriftinghedonicsLag}{+0.06} | |
| 937 | +\newcommand{\MCprovinceAmediandriftinghedonicsRev}{n/a} | |
| 938 | +\newcommand{\MCprovinceAmediandriftinghedonicsRmse}{3.69} | |
| 939 | +\newcommand{\MCprovinceAmediandriftinghedonicsShare}{100} | |
| 940 | +\newcommand{\MCprovinceAmedianflatqualityupBias}{0.265} | |
| 941 | +\newcommand{\MCprovinceAmedianflatqualityupCov}{n/a} | |
| 942 | +\newcommand{\MCprovinceAmedianflatqualityupLag}{-0.05} | |
| 943 | +\newcommand{\MCprovinceAmedianflatqualityupRev}{n/a} | |
| 944 | +\newcommand{\MCprovinceAmedianflatqualityupRmse}{3.62} | |
| 945 | +\newcommand{\MCprovinceAmedianflatqualityupShare}{100} | |
| 946 | +\newcommand{\MCprovinceAmedianheavytailsBias}{-0.002} | |
| 947 | +\newcommand{\MCprovinceAmedianheavytailsCov}{n/a} | |
| 948 | +\newcommand{\MCprovinceAmedianheavytailsLag}{-0.06} | |
| 949 | +\newcommand{\MCprovinceAmedianheavytailsRev}{n/a} | |
| 950 | +\newcommand{\MCprovinceAmedianheavytailsRmse}{3.59} | |
| 951 | +\newcommand{\MCprovinceAmedianheavytailsShare}{100} | |
| 952 | +\newcommand{\MCprovinceAmedianidiosyncratictownsBias}{-0.003} | |
| 953 | +\newcommand{\MCprovinceAmedianidiosyncratictownsCov}{n/a} | |
| 954 | +\newcommand{\MCprovinceAmedianidiosyncratictownsLag}{-0.01} | |
| 955 | +\newcommand{\MCprovinceAmedianidiosyncratictownsRev}{n/a} | |
| 956 | +\newcommand{\MCprovinceAmedianidiosyncratictownsRmse}{3.62} | |
| 957 | +\newcommand{\MCprovinceAmedianidiosyncratictownsShare}{100} | |
| 958 | +\newcommand{\MCprovinceAmediannolocaldynamicsBias}{0.008} | |
| 959 | +\newcommand{\MCprovinceAmediannolocaldynamicsCov}{n/a} | |
| 960 | +\newcommand{\MCprovinceAmediannolocaldynamicsLag}{+0.04} | |
| 961 | +\newcommand{\MCprovinceAmediannolocaldynamicsRev}{n/a} | |
| 962 | +\newcommand{\MCprovinceAmediannolocaldynamicsRmse}{3.56} | |
| 963 | +\newcommand{\MCprovinceAmediannolocaldynamicsShare}{100} | |
| 964 | +\newcommand{\MCprovinceAmedianrisingqualitydownBias}{-0.227} | |
| 965 | +\newcommand{\MCprovinceAmedianrisingqualitydownCov}{n/a} | |
| 966 | +\newcommand{\MCprovinceAmedianrisingqualitydownLag}{+0.10} | |
| 967 | +\newcommand{\MCprovinceAmedianrisingqualitydownRev}{n/a} | |
| 968 | +\newcommand{\MCprovinceAmedianrisingqualitydownRmse}{3.55} | |
| 969 | +\newcommand{\MCprovinceAmedianrisingqualitydownShare}{100} | |
| 970 | +\newcommand{\MCprovinceAmediansparseBias}{-0.006} | |
| 971 | +\newcommand{\MCprovinceAmediansparseCov}{n/a} | |
| 972 | +\newcommand{\MCprovinceAmediansparseLag}{+0.10} | |
| 973 | +\newcommand{\MCprovinceAmediansparseRev}{n/a} | |
| 974 | +\newcommand{\MCprovinceAmediansparseRmse}{6.07} | |
| 975 | +\newcommand{\MCprovinceAmediansparseShare}{100} | |
| 976 | +\newcommand{\MCprovinceAmedianturningpointBias}{0.010} | |
| 977 | +\newcommand{\MCprovinceAmedianturningpointCov}{n/a} | |
| 978 | +\newcommand{\MCprovinceAmedianturningpointLag}{+0.09} | |
| 979 | +\newcommand{\MCprovinceAmedianturningpointRev}{n/a} | |
| 980 | +\newcommand{\MCprovinceAmedianturningpointRmse}{3.56} | |
| 981 | +\newcommand{\MCprovinceAmedianturningpointShare}{100} | |
| 982 | +\newcommand{\MCprovinceBstratifiedbaselineBias}{0.024} | |
| 983 | +\newcommand{\MCprovinceBstratifiedbaselineCov}{n/a} | |
| 984 | +\newcommand{\MCprovinceBstratifiedbaselineLag}{+0.05} | |
| 985 | +\newcommand{\MCprovinceBstratifiedbaselineRev}{n/a} | |
| 986 | +\newcommand{\MCprovinceBstratifiedbaselineRmse}{3.08} | |
| 987 | +\newcommand{\MCprovinceBstratifiedbaselineShare}{100} | |
| 988 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsBias}{0.024} | |
| 989 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsCov}{n/a} | |
| 990 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsLag}{+0.03} | |
| 991 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsRev}{n/a} | |
| 992 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsRmse}{3.14} | |
| 993 | +\newcommand{\MCprovinceBstratifieddriftinghedonicsShare}{100} | |
| 994 | +\newcommand{\MCprovinceBstratifiedflatqualityupBias}{0.090} | |
| 995 | +\newcommand{\MCprovinceBstratifiedflatqualityupCov}{n/a} | |
| 996 | +\newcommand{\MCprovinceBstratifiedflatqualityupLag}{+0.05} | |
| 997 | +\newcommand{\MCprovinceBstratifiedflatqualityupRev}{n/a} | |
| 998 | +\newcommand{\MCprovinceBstratifiedflatqualityupRmse}{3.13} | |
| 999 | +\newcommand{\MCprovinceBstratifiedflatqualityupShare}{100} | |
| 1000 | +\newcommand{\MCprovinceBstratifiedheavytailsBias}{0.001} | |
| 1001 | +\newcommand{\MCprovinceBstratifiedheavytailsCov}{n/a} | |
| 1002 | +\newcommand{\MCprovinceBstratifiedheavytailsLag}{-0.04} | |
| 1003 | +\newcommand{\MCprovinceBstratifiedheavytailsRev}{n/a} | |
| 1004 | +\newcommand{\MCprovinceBstratifiedheavytailsRmse}{3.11} | |
| 1005 | +\newcommand{\MCprovinceBstratifiedheavytailsShare}{100} | |
| 1006 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsBias}{-0.007} | |
| 1007 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsCov}{n/a} | |
| 1008 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsLag}{-0.01} | |
| 1009 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsRev}{n/a} | |
| 1010 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsRmse}{3.15} | |
| 1011 | +\newcommand{\MCprovinceBstratifiedidiosyncratictownsShare}{100} | |
| 1012 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsBias}{0.022} | |
| 1013 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsCov}{n/a} | |
| 1014 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsLag}{+0.01} | |
| 1015 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsRev}{n/a} | |
| 1016 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsRmse}{3.07} | |
| 1017 | +\newcommand{\MCprovinceBstratifiednolocaldynamicsShare}{100} | |
| 1018 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownBias}{-0.033} | |
| 1019 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownCov}{n/a} | |
| 1020 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownLag}{+0.01} | |
| 1021 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownRev}{n/a} | |
| 1022 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownRmse}{3.06} | |
| 1023 | +\newcommand{\MCprovinceBstratifiedrisingqualitydownShare}{100} | |
| 1024 | +\newcommand{\MCprovinceBstratifiedsparseBias}{0.006} | |
| 1025 | +\newcommand{\MCprovinceBstratifiedsparseCov}{n/a} | |
| 1026 | +\newcommand{\MCprovinceBstratifiedsparseLag}{-0.04} | |
| 1027 | +\newcommand{\MCprovinceBstratifiedsparseRev}{n/a} | |
| 1028 | +\newcommand{\MCprovinceBstratifiedsparseRmse}{5.21} | |
| 1029 | +\newcommand{\MCprovinceBstratifiedsparseShare}{100} | |
| 1030 | +\newcommand{\MCprovinceBstratifiedturningpointBias}{0.024} | |
| 1031 | +\newcommand{\MCprovinceBstratifiedturningpointCov}{n/a} | |
| 1032 | +\newcommand{\MCprovinceBstratifiedturningpointLag}{+0.00} | |
| 1033 | +\newcommand{\MCprovinceBstratifiedturningpointRev}{n/a} | |
| 1034 | +\newcommand{\MCprovinceBstratifiedturningpointRmse}{3.08} | |
| 1035 | +\newcommand{\MCprovinceBstratifiedturningpointShare}{100} | |
| 1036 | +\newcommand{\MCprovinceCpooledtdbaselineBias}{0.000} | |
| 1037 | +\newcommand{\MCprovinceCpooledtdbaselineCov}{98} | |
| 1038 | +\newcommand{\MCprovinceCpooledtdbaselineLag}{+0.00} | |
| 1039 | +\newcommand{\MCprovinceCpooledtdbaselineRev}{n/a} | |
| 1040 | +\newcommand{\MCprovinceCpooledtdbaselineRmse}{1.24} | |
| 1041 | +\newcommand{\MCprovinceCpooledtdbaselineShare}{100} | |
| 1042 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsBias}{0.001} | |
| 1043 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsCov}{98} | |
| 1044 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsLag}{+0.00} | |
| 1045 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsRev}{n/a} | |
| 1046 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsRmse}{1.29} | |
| 1047 | +\newcommand{\MCprovinceCpooledtddriftinghedonicsShare}{100} | |
| 1048 | +\newcommand{\MCprovinceCpooledtdflatqualityupBias}{0.045} | |
| 1049 | +\newcommand{\MCprovinceCpooledtdflatqualityupCov}{97} | |
| 1050 | +\newcommand{\MCprovinceCpooledtdflatqualityupLag}{+0.01} | |
| 1051 | +\newcommand{\MCprovinceCpooledtdflatqualityupRev}{n/a} | |
| 1052 | +\newcommand{\MCprovinceCpooledtdflatqualityupRmse}{1.25} | |
| 1053 | +\newcommand{\MCprovinceCpooledtdflatqualityupShare}{100} | |
| 1054 | +\newcommand{\MCprovinceCpooledtdheavytailsBias}{-0.005} | |
| 1055 | +\newcommand{\MCprovinceCpooledtdheavytailsCov}{98} | |
| 1056 | +\newcommand{\MCprovinceCpooledtdheavytailsLag}{+0.00} | |
| 1057 | +\newcommand{\MCprovinceCpooledtdheavytailsRev}{n/a} | |
| 1058 | +\newcommand{\MCprovinceCpooledtdheavytailsRmse}{1.24} | |
| 1059 | +\newcommand{\MCprovinceCpooledtdheavytailsShare}{100} | |
| 1060 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsBias}{-0.001} | |
| 1061 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsCov}{98} | |
| 1062 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsLag}{+0.00} | |
| 1063 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsRev}{n/a} | |
| 1064 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsRmse}{1.37} | |
| 1065 | +\newcommand{\MCprovinceCpooledtdidiosyncratictownsShare}{100} | |
| 1066 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsBias}{-0.000} | |
| 1067 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsCov}{98} | |
| 1068 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsLag}{+0.00} | |
| 1069 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsRev}{n/a} | |
| 1070 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsRmse}{1.22} | |
| 1071 | +\newcommand{\MCprovinceCpooledtdnolocaldynamicsShare}{100} | |
| 1072 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownBias}{-0.043} | |
| 1073 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownCov}{97} | |
| 1074 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownLag}{+0.00} | |
| 1075 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownRev}{n/a} | |
| 1076 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownRmse}{1.24} | |
| 1077 | +\newcommand{\MCprovinceCpooledtdrisingqualitydownShare}{100} | |
| 1078 | +\newcommand{\MCprovinceCpooledtdsparseBias}{-0.003} | |
| 1079 | +\newcommand{\MCprovinceCpooledtdsparseCov}{98} | |
| 1080 | +\newcommand{\MCprovinceCpooledtdsparseLag}{+0.00} | |
| 1081 | +\newcommand{\MCprovinceCpooledtdsparseRev}{n/a} | |
| 1082 | +\newcommand{\MCprovinceCpooledtdsparseRmse}{2.13} | |
| 1083 | +\newcommand{\MCprovinceCpooledtdsparseShare}{100} | |
| 1084 | +\newcommand{\MCprovinceCpooledtdturningpointBias}{0.000} | |
| 1085 | +\newcommand{\MCprovinceCpooledtdturningpointCov}{98} | |
| 1086 | +\newcommand{\MCprovinceCpooledtdturningpointLag}{+0.00} | |
| 1087 | +\newcommand{\MCprovinceCpooledtdturningpointRev}{n/a} | |
| 1088 | +\newcommand{\MCprovinceCpooledtdturningpointRmse}{1.24} | |
| 1089 | +\newcommand{\MCprovinceCpooledtdturningpointShare}{100} | |
| 1090 | +\newcommand{\MCprovinceDrtdOneThreebaselineBias}{-0.003} | |
| 1091 | +\newcommand{\MCprovinceDrtdOneThreebaselineCov}{n/a} | |
| 1092 | +\newcommand{\MCprovinceDrtdOneThreebaselineLag}{+0.00} | |
| 1093 | +\newcommand{\MCprovinceDrtdOneThreebaselineRev}{n/a} | |
| 1094 | +\newcommand{\MCprovinceDrtdOneThreebaselineRmse}{1.39} | |
| 1095 | +\newcommand{\MCprovinceDrtdOneThreebaselineShare}{100} | |
| 1096 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsBias}{-0.002} | |
| 1097 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsCov}{n/a} | |
| 1098 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsLag}{+0.00} | |
| 1099 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsRev}{n/a} | |
| 1100 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsRmse}{1.44} | |
| 1101 | +\newcommand{\MCprovinceDrtdOneThreedriftinghedonicsShare}{100} | |
| 1102 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupBias}{0.042} | |
| 1103 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupCov}{n/a} | |
| 1104 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupLag}{+0.03} | |
| 1105 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupRev}{n/a} | |
| 1106 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupRmse}{1.40} | |
| 1107 | +\newcommand{\MCprovinceDrtdOneThreeflatqualityupShare}{100} | |
| 1108 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsBias}{-0.005} | |
| 1109 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsCov}{n/a} | |
| 1110 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsLag}{+0.00} | |
| 1111 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsRev}{n/a} | |
| 1112 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsRmse}{1.41} | |
| 1113 | +\newcommand{\MCprovinceDrtdOneThreeheavytailsShare}{100} | |
| 1114 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsBias}{-0.006} | |
| 1115 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsCov}{n/a} | |
| 1116 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsLag}{+0.00} | |
| 1117 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsRev}{n/a} | |
| 1118 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsRmse}{1.44} | |
| 1119 | +\newcommand{\MCprovinceDrtdOneThreeidiosyncratictownsShare}{100} | |
| 1120 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsBias}{-0.003} | |
| 1121 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsCov}{n/a} | |
| 1122 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsLag}{+0.00} | |
| 1123 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsRev}{n/a} | |
| 1124 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsRmse}{1.39} | |
| 1125 | +\newcommand{\MCprovinceDrtdOneThreenolocaldynamicsShare}{100} | |
| 1126 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownBias}{-0.045} | |
| 1127 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownCov}{n/a} | |
| 1128 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownLag}{+0.00} | |
| 1129 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownRev}{n/a} | |
| 1130 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownRmse}{1.39} | |
| 1131 | +\newcommand{\MCprovinceDrtdOneThreerisingqualitydownShare}{100} | |
| 1132 | +\newcommand{\MCprovinceDrtdOneThreesparseBias}{-0.009} | |
| 1133 | +\newcommand{\MCprovinceDrtdOneThreesparseCov}{n/a} | |
| 1134 | +\newcommand{\MCprovinceDrtdOneThreesparseLag}{+0.00} | |
| 1135 | +\newcommand{\MCprovinceDrtdOneThreesparseRev}{n/a} | |
| 1136 | +\newcommand{\MCprovinceDrtdOneThreesparseRmse}{2.36} | |
| 1137 | +\newcommand{\MCprovinceDrtdOneThreesparseShare}{100} | |
| 1138 | +\newcommand{\MCprovinceDrtdOneThreeturningpointBias}{-0.003} | |
| 1139 | +\newcommand{\MCprovinceDrtdOneThreeturningpointCov}{n/a} | |
| 1140 | +\newcommand{\MCprovinceDrtdOneThreeturningpointLag}{+0.00} | |
| 1141 | +\newcommand{\MCprovinceDrtdOneThreeturningpointRev}{n/a} | |
| 1142 | +\newcommand{\MCprovinceDrtdOneThreeturningpointRmse}{1.39} | |
| 1143 | +\newcommand{\MCprovinceDrtdOneThreeturningpointShare}{100} | |
| 1144 | +\newcommand{\MCprovinceHrepeatsalesbaselineBias}{-0.007} | |
| 1145 | +\newcommand{\MCprovinceHrepeatsalesbaselineCov}{n/a} | |
| 1146 | +\newcommand{\MCprovinceHrepeatsalesbaselineLag}{-0.03} | |
| 1147 | +\newcommand{\MCprovinceHrepeatsalesbaselineRev}{n/a} | |
| 1148 | +\newcommand{\MCprovinceHrepeatsalesbaselineRmse}{5.32} | |
| 1149 | +\newcommand{\MCprovinceHrepeatsalesbaselineShare}{100} | |
| 1150 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsBias}{-0.004} | |
| 1151 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsCov}{n/a} | |
| 1152 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsLag}{+0.01} | |
| 1153 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsRev}{n/a} | |
| 1154 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsRmse}{5.37} | |
| 1155 | +\newcommand{\MCprovinceHrepeatsalesdriftinghedonicsShare}{100} | |
| 1156 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupBias}{0.041} | |
| 1157 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupCov}{n/a} | |
| 1158 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupLag}{-0.17} | |
| 1159 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupRev}{n/a} | |
| 1160 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupRmse}{5.33} | |
| 1161 | +\newcommand{\MCprovinceHrepeatsalesflatqualityupShare}{100} | |
| 1162 | +\newcommand{\MCprovinceHrepeatsalesheavytailsBias}{-0.016} | |
| 1163 | +\newcommand{\MCprovinceHrepeatsalesheavytailsCov}{n/a} | |
| 1164 | +\newcommand{\MCprovinceHrepeatsalesheavytailsLag}{+0.25} | |
| 1165 | +\newcommand{\MCprovinceHrepeatsalesheavytailsRev}{n/a} | |
| 1166 | +\newcommand{\MCprovinceHrepeatsalesheavytailsRmse}{7.42} | |
| 1167 | +\newcommand{\MCprovinceHrepeatsalesheavytailsShare}{100} | |
| 1168 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsBias}{0.007} | |
| 1169 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsCov}{n/a} | |
| 1170 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsLag}{+0.15} | |
| 1171 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsRev}{n/a} | |
| 1172 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsRmse}{5.66} | |
| 1173 | +\newcommand{\MCprovinceHrepeatsalesidiosyncratictownsShare}{100} | |
| 1174 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsBias}{-0.008} | |
| 1175 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsCov}{n/a} | |
| 1176 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsLag}{-0.03} | |
| 1177 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsRev}{n/a} | |
| 1178 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsRmse}{5.28} | |
| 1179 | +\newcommand{\MCprovinceHrepeatsalesnolocaldynamicsShare}{100} | |
| 1180 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownBias}{-0.039} | |
| 1181 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownCov}{n/a} | |
| 1182 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownLag}{-0.01} | |
| 1183 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownRev}{n/a} | |
| 1184 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownRmse}{5.33} | |
| 1185 | +\newcommand{\MCprovinceHrepeatsalesrisingqualitydownShare}{100} | |
| 1186 | +\newcommand{\MCprovinceHrepeatsalessparseBias}{-0.008} | |
| 1187 | +\newcommand{\MCprovinceHrepeatsalessparseCov}{n/a} | |
| 1188 | +\newcommand{\MCprovinceHrepeatsalessparseLag}{-0.10} | |
| 1189 | +\newcommand{\MCprovinceHrepeatsalessparseRev}{n/a} | |
| 1190 | +\newcommand{\MCprovinceHrepeatsalessparseRmse}{9.57} | |
| 1191 | +\newcommand{\MCprovinceHrepeatsalessparseShare}{100} | |
| 1192 | +\newcommand{\MCprovinceHrepeatsalesturningpointBias}{-0.007} | |
| 1193 | +\newcommand{\MCprovinceHrepeatsalesturningpointCov}{n/a} | |
| 1194 | +\newcommand{\MCprovinceHrepeatsalesturningpointLag}{-0.07} | |
| 1195 | +\newcommand{\MCprovinceHrepeatsalesturningpointRev}{n/a} | |
| 1196 | +\newcommand{\MCprovinceHrepeatsalesturningpointRmse}{5.32} | |
| 1197 | +\newcommand{\MCprovinceHrepeatsalesturningpointShare}{100} | |
| 1198 | +\newcommand{\MCprovinceKqhpibaselineBias}{-0.003} | |
| 1199 | +\newcommand{\MCprovinceKqhpibaselineCov}{n/a} | |
| 1200 | +\newcommand{\MCprovinceKqhpibaselineLag}{+0.00} | |
| 1201 | +\newcommand{\MCprovinceKqhpibaselineRev}{n/a} | |
| 1202 | +\newcommand{\MCprovinceKqhpibaselineRmse}{1.39} | |
| 1203 | +\newcommand{\MCprovinceKqhpibaselineShare}{100} | |
| 1204 | +\newcommand{\MCprovinceKqhpidriftinghedonicsBias}{-0.002} | |
| 1205 | +\newcommand{\MCprovinceKqhpidriftinghedonicsCov}{n/a} | |
| 1206 | +\newcommand{\MCprovinceKqhpidriftinghedonicsLag}{+0.00} | |
| 1207 | +\newcommand{\MCprovinceKqhpidriftinghedonicsRev}{n/a} | |
| 1208 | +\newcommand{\MCprovinceKqhpidriftinghedonicsRmse}{1.44} | |
| 1209 | +\newcommand{\MCprovinceKqhpidriftinghedonicsShare}{100} | |
| 1210 | +\newcommand{\MCprovinceKqhpiflatqualityupBias}{0.042} | |
| 1211 | +\newcommand{\MCprovinceKqhpiflatqualityupCov}{n/a} | |
| 1212 | +\newcommand{\MCprovinceKqhpiflatqualityupLag}{+0.03} | |
| 1213 | +\newcommand{\MCprovinceKqhpiflatqualityupRev}{n/a} | |
| 1214 | +\newcommand{\MCprovinceKqhpiflatqualityupRmse}{1.40} | |
| 1215 | +\newcommand{\MCprovinceKqhpiflatqualityupShare}{100} | |
| 1216 | +\newcommand{\MCprovinceKqhpiheavytailsBias}{-0.005} | |
| 1217 | +\newcommand{\MCprovinceKqhpiheavytailsCov}{n/a} | |
| 1218 | +\newcommand{\MCprovinceKqhpiheavytailsLag}{+0.00} | |
| 1219 | +\newcommand{\MCprovinceKqhpiheavytailsRev}{n/a} | |
| 1220 | +\newcommand{\MCprovinceKqhpiheavytailsRmse}{1.41} | |
| 1221 | +\newcommand{\MCprovinceKqhpiheavytailsShare}{100} | |
| 1222 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsBias}{-0.006} | |
| 1223 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsCov}{n/a} | |
| 1224 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsLag}{+0.00} | |
| 1225 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsRev}{n/a} | |
| 1226 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsRmse}{1.44} | |
| 1227 | +\newcommand{\MCprovinceKqhpiidiosyncratictownsShare}{100} | |
| 1228 | +\newcommand{\MCprovinceKqhpinolocaldynamicsBias}{-0.003} | |
| 1229 | +\newcommand{\MCprovinceKqhpinolocaldynamicsCov}{n/a} | |
| 1230 | +\newcommand{\MCprovinceKqhpinolocaldynamicsLag}{+0.00} | |
| 1231 | +\newcommand{\MCprovinceKqhpinolocaldynamicsRev}{n/a} | |
| 1232 | +\newcommand{\MCprovinceKqhpinolocaldynamicsRmse}{1.39} | |
| 1233 | +\newcommand{\MCprovinceKqhpinolocaldynamicsShare}{100} | |
| 1234 | +\newcommand{\MCprovinceKqhpirisingqualitydownBias}{-0.045} | |
| 1235 | +\newcommand{\MCprovinceKqhpirisingqualitydownCov}{n/a} | |
| 1236 | +\newcommand{\MCprovinceKqhpirisingqualitydownLag}{+0.00} | |
| 1237 | +\newcommand{\MCprovinceKqhpirisingqualitydownRev}{n/a} | |
| 1238 | +\newcommand{\MCprovinceKqhpirisingqualitydownRmse}{1.39} | |
| 1239 | +\newcommand{\MCprovinceKqhpirisingqualitydownShare}{100} | |
| 1240 | +\newcommand{\MCprovinceKqhpisparseBias}{-0.009} | |
| 1241 | +\newcommand{\MCprovinceKqhpisparseCov}{n/a} | |
| 1242 | +\newcommand{\MCprovinceKqhpisparseLag}{+0.00} | |
| 1243 | +\newcommand{\MCprovinceKqhpisparseRev}{n/a} | |
| 1244 | +\newcommand{\MCprovinceKqhpisparseRmse}{2.36} | |
| 1245 | +\newcommand{\MCprovinceKqhpisparseShare}{100} | |
| 1246 | +\newcommand{\MCprovinceKqhpiturningpointBias}{-0.003} | |
| 1247 | +\newcommand{\MCprovinceKqhpiturningpointCov}{n/a} | |
| 1248 | +\newcommand{\MCprovinceKqhpiturningpointLag}{+0.00} | |
| 1249 | +\newcommand{\MCprovinceKqhpiturningpointRev}{n/a} | |
| 1250 | +\newcommand{\MCprovinceKqhpiturningpointRmse}{1.39} | |
| 1251 | +\newcommand{\MCprovinceKqhpiturningpointShare}{100} | |
| 1252 | +\newcommand{\MCprovinceSLLTbaselineBias}{0.001} | |
| 1253 | +\newcommand{\MCprovinceSLLTbaselineCov}{98} | |
| 1254 | +\newcommand{\MCprovinceSLLTbaselineLag}{+0.00} | |
| 1255 | +\newcommand{\MCprovinceSLLTbaselineRev}{n/a} | |
| 1256 | +\newcommand{\MCprovinceSLLTbaselineRmse}{1.25} | |
| 1257 | +\newcommand{\MCprovinceSLLTbaselineShare}{100} | |
| 1258 | +\newcommand{\MCprovinceSLLTdriftinghedonicsBias}{0.001} | |
| 1259 | +\newcommand{\MCprovinceSLLTdriftinghedonicsCov}{98} | |
| 1260 | +\newcommand{\MCprovinceSLLTdriftinghedonicsLag}{+0.00} | |
| 1261 | +\newcommand{\MCprovinceSLLTdriftinghedonicsRev}{n/a} | |
| 1262 | +\newcommand{\MCprovinceSLLTdriftinghedonicsRmse}{1.29} | |
| 1263 | +\newcommand{\MCprovinceSLLTdriftinghedonicsShare}{100} | |
| 1264 | +\newcommand{\MCprovinceSLLTflatqualityupBias}{0.049} | |
| 1265 | +\newcommand{\MCprovinceSLLTflatqualityupCov}{96} | |
| 1266 | +\newcommand{\MCprovinceSLLTflatqualityupLag}{+0.01} | |
| 1267 | +\newcommand{\MCprovinceSLLTflatqualityupRev}{n/a} | |
| 1268 | +\newcommand{\MCprovinceSLLTflatqualityupRmse}{1.25} | |
| 1269 | +\newcommand{\MCprovinceSLLTflatqualityupShare}{100} | |
| 1270 | +\newcommand{\MCprovinceSLLTheavytailsBias}{-0.003} | |
| 1271 | +\newcommand{\MCprovinceSLLTheavytailsCov}{98} | |
| 1272 | +\newcommand{\MCprovinceSLLTheavytailsLag}{+0.00} | |
| 1273 | +\newcommand{\MCprovinceSLLTheavytailsRev}{n/a} | |
| 1274 | +\newcommand{\MCprovinceSLLTheavytailsRmse}{1.26} | |
| 1275 | +\newcommand{\MCprovinceSLLTheavytailsShare}{100} | |
| 1276 | +\newcommand{\MCprovinceSLLTidiosyncratictownsBias}{0.000} | |
| 1277 | +\newcommand{\MCprovinceSLLTidiosyncratictownsCov}{98} | |
| 1278 | +\newcommand{\MCprovinceSLLTidiosyncratictownsLag}{+0.00} | |
| 1279 | +\newcommand{\MCprovinceSLLTidiosyncratictownsRev}{n/a} | |
| 1280 | +\newcommand{\MCprovinceSLLTidiosyncratictownsRmse}{1.37} | |
| 1281 | +\newcommand{\MCprovinceSLLTidiosyncratictownsShare}{100} | |
| 1282 | +\newcommand{\MCprovinceSLLTnolocaldynamicsBias}{0.001} | |
| 1283 | +\newcommand{\MCprovinceSLLTnolocaldynamicsCov}{98} | |
| 1284 | +\newcommand{\MCprovinceSLLTnolocaldynamicsLag}{+0.00} | |
| 1285 | +\newcommand{\MCprovinceSLLTnolocaldynamicsRev}{n/a} | |
| 1286 | +\newcommand{\MCprovinceSLLTnolocaldynamicsRmse}{1.22} | |
| 1287 | +\newcommand{\MCprovinceSLLTnolocaldynamicsShare}{100} | |
| 1288 | +\newcommand{\MCprovinceSLLTrisingqualitydownBias}{-0.044} | |
| 1289 | +\newcommand{\MCprovinceSLLTrisingqualitydownCov}{97} | |
| 1290 | +\newcommand{\MCprovinceSLLTrisingqualitydownLag}{+0.00} | |
| 1291 | +\newcommand{\MCprovinceSLLTrisingqualitydownRev}{n/a} | |
| 1292 | +\newcommand{\MCprovinceSLLTrisingqualitydownRmse}{1.25} | |
| 1293 | +\newcommand{\MCprovinceSLLTrisingqualitydownShare}{100} | |
| 1294 | +\newcommand{\MCprovinceSLLTsparseBias}{-0.002} | |
| 1295 | +\newcommand{\MCprovinceSLLTsparseCov}{98} | |
| 1296 | +\newcommand{\MCprovinceSLLTsparseLag}{+0.00} | |
| 1297 | +\newcommand{\MCprovinceSLLTsparseRev}{n/a} | |
| 1298 | +\newcommand{\MCprovinceSLLTsparseRmse}{2.14} | |
| 1299 | +\newcommand{\MCprovinceSLLTsparseShare}{100} | |
| 1300 | +\newcommand{\MCprovinceSLLTturningpointBias}{0.001} | |
| 1301 | +\newcommand{\MCprovinceSLLTturningpointCov}{98} | |
| 1302 | +\newcommand{\MCprovinceSLLTturningpointLag}{+0.00} | |
| 1303 | +\newcommand{\MCprovinceSLLTturningpointRev}{n/a} | |
| 1304 | +\newcommand{\MCprovinceSLLTturningpointRmse}{1.25} | |
| 1305 | +\newcommand{\MCprovinceSLLTturningpointShare}{100} | |
| 1306 | +\newcommand{\MCprovinceScoarselocbaselineBias}{0.009} | |
| 1307 | +\newcommand{\MCprovinceScoarselocbaselineCov}{98} | |
| 1308 | +\newcommand{\MCprovinceScoarselocbaselineLag}{+0.00} | |
| 1309 | +\newcommand{\MCprovinceScoarselocbaselineRev}{n/a} | |
| 1310 | +\newcommand{\MCprovinceScoarselocbaselineRmse}{1.88} | |
| 1311 | +\newcommand{\MCprovinceScoarselocbaselineShare}{100} | |
| 1312 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsBias}{0.010} | |
| 1313 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsCov}{98} | |
| 1314 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsLag}{+0.00} | |
| 1315 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsRev}{n/a} | |
| 1316 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsRmse}{1.92} | |
| 1317 | +\newcommand{\MCprovinceScoarselocdriftinghedonicsShare}{100} | |
| 1318 | +\newcommand{\MCprovinceScoarselocflatqualityupBias}{0.044} | |
| 1319 | +\newcommand{\MCprovinceScoarselocflatqualityupCov}{97} | |
| 1320 | +\newcommand{\MCprovinceScoarselocflatqualityupLag}{+0.07} | |
| 1321 | +\newcommand{\MCprovinceScoarselocflatqualityupRev}{n/a} | |
| 1322 | +\newcommand{\MCprovinceScoarselocflatqualityupRmse}{1.88} | |
| 1323 | +\newcommand{\MCprovinceScoarselocflatqualityupShare}{100} | |
| 1324 | +\newcommand{\MCprovinceScoarselocheavytailsBias}{0.002} | |
| 1325 | +\newcommand{\MCprovinceScoarselocheavytailsCov}{98} | |
| 1326 | +\newcommand{\MCprovinceScoarselocheavytailsLag}{+0.01} | |
| 1327 | +\newcommand{\MCprovinceScoarselocheavytailsRev}{n/a} | |
| 1328 | +\newcommand{\MCprovinceScoarselocheavytailsRmse}{1.96} | |
| 1329 | +\newcommand{\MCprovinceScoarselocheavytailsShare}{100} | |
| 1330 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsBias}{-0.004} | |
| 1331 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsCov}{98} | |
| 1332 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsLag}{-0.01} | |
| 1333 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsRev}{n/a} | |
| 1334 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsRmse}{1.96} | |
| 1335 | +\newcommand{\MCprovinceScoarselocidiosyncratictownsShare}{100} | |
| 1336 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsBias}{0.008} | |
| 1337 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsCov}{98} | |
| 1338 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsLag}{+0.00} | |
| 1339 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsRev}{n/a} | |
| 1340 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsRmse}{1.87} | |
| 1341 | +\newcommand{\MCprovinceScoarselocnolocaldynamicsShare}{100} | |
| 1342 | +\newcommand{\MCprovinceScoarselocrisingqualitydownBias}{-0.023} | |
| 1343 | +\newcommand{\MCprovinceScoarselocrisingqualitydownCov}{98} | |
| 1344 | +\newcommand{\MCprovinceScoarselocrisingqualitydownLag}{+0.00} | |
| 1345 | +\newcommand{\MCprovinceScoarselocrisingqualitydownRev}{n/a} | |
| 1346 | +\newcommand{\MCprovinceScoarselocrisingqualitydownRmse}{1.88} | |
| 1347 | +\newcommand{\MCprovinceScoarselocrisingqualitydownShare}{100} | |
| 1348 | +\newcommand{\MCprovinceScoarselocsparseBias}{-0.008} | |
| 1349 | +\newcommand{\MCprovinceScoarselocsparseCov}{98} | |
| 1350 | +\newcommand{\MCprovinceScoarselocsparseLag}{+0.06} | |
| 1351 | +\newcommand{\MCprovinceScoarselocsparseRev}{n/a} | |
| 1352 | +\newcommand{\MCprovinceScoarselocsparseRmse}{3.18} | |
| 1353 | +\newcommand{\MCprovinceScoarselocsparseShare}{100} | |
| 1354 | +\newcommand{\MCprovinceScoarselocturningpointBias}{0.009} | |
| 1355 | +\newcommand{\MCprovinceScoarselocturningpointCov}{98} | |
| 1356 | +\newcommand{\MCprovinceScoarselocturningpointLag}{+0.00} | |
| 1357 | +\newcommand{\MCprovinceScoarselocturningpointRev}{n/a} | |
| 1358 | +\newcommand{\MCprovinceScoarselocturningpointRmse}{1.88} | |
| 1359 | +\newcommand{\MCprovinceScoarselocturningpointShare}{100} | |
| 1360 | +\newcommand{\MCprovinceSnoEBbaselineBias}{0.001} | |
| 1361 | +\newcommand{\MCprovinceSnoEBbaselineCov}{98} | |
| 1362 | +\newcommand{\MCprovinceSnoEBbaselineLag}{+0.00} | |
| 1363 | +\newcommand{\MCprovinceSnoEBbaselineRev}{n/a} | |
| 1364 | +\newcommand{\MCprovinceSnoEBbaselineRmse}{1.25} | |
| 1365 | +\newcommand{\MCprovinceSnoEBbaselineShare}{100} | |
| 1366 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsBias}{0.001} | |
| 1367 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsCov}{98} | |
| 1368 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsLag}{+0.00} | |
| 1369 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsRev}{n/a} | |
| 1370 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsRmse}{1.29} | |
| 1371 | +\newcommand{\MCprovinceSnoEBdriftinghedonicsShare}{100} | |
| 1372 | +\newcommand{\MCprovinceSnoEBflatqualityupBias}{0.049} | |
| 1373 | +\newcommand{\MCprovinceSnoEBflatqualityupCov}{96} | |
| 1374 | +\newcommand{\MCprovinceSnoEBflatqualityupLag}{+0.01} | |
| 1375 | +\newcommand{\MCprovinceSnoEBflatqualityupRev}{n/a} | |
| 1376 | +\newcommand{\MCprovinceSnoEBflatqualityupRmse}{1.25} | |
| 1377 | +\newcommand{\MCprovinceSnoEBflatqualityupShare}{100} | |
| 1378 | +\newcommand{\MCprovinceSnoEBheavytailsBias}{-0.003} | |
| 1379 | +\newcommand{\MCprovinceSnoEBheavytailsCov}{98} | |
| 1380 | +\newcommand{\MCprovinceSnoEBheavytailsLag}{+0.00} | |
| 1381 | +\newcommand{\MCprovinceSnoEBheavytailsRev}{n/a} | |
| 1382 | +\newcommand{\MCprovinceSnoEBheavytailsRmse}{1.26} | |
| 1383 | +\newcommand{\MCprovinceSnoEBheavytailsShare}{100} | |
| 1384 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsBias}{0.000} | |
| 1385 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsCov}{98} | |
| 1386 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsLag}{+0.00} | |
| 1387 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsRev}{n/a} | |
| 1388 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsRmse}{1.37} | |
| 1389 | +\newcommand{\MCprovinceSnoEBidiosyncratictownsShare}{100} | |
| 1390 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsBias}{0.001} | |
| 1391 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsCov}{98} | |
| 1392 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsLag}{+0.00} | |
| 1393 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsRev}{n/a} | |
| 1394 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsRmse}{1.22} | |
| 1395 | +\newcommand{\MCprovinceSnoEBnolocaldynamicsShare}{100} | |
| 1396 | +\newcommand{\MCprovinceSnoEBrisingqualitydownBias}{-0.044} | |
| 1397 | +\newcommand{\MCprovinceSnoEBrisingqualitydownCov}{97} | |
| 1398 | +\newcommand{\MCprovinceSnoEBrisingqualitydownLag}{+0.00} | |
| 1399 | +\newcommand{\MCprovinceSnoEBrisingqualitydownRev}{n/a} | |
| 1400 | +\newcommand{\MCprovinceSnoEBrisingqualitydownRmse}{1.25} | |
| 1401 | +\newcommand{\MCprovinceSnoEBrisingqualitydownShare}{100} | |
| 1402 | +\newcommand{\MCprovinceSnoEBsparseBias}{-0.002} | |
| 1403 | +\newcommand{\MCprovinceSnoEBsparseCov}{98} | |
| 1404 | +\newcommand{\MCprovinceSnoEBsparseLag}{+0.00} | |
| 1405 | +\newcommand{\MCprovinceSnoEBsparseRev}{n/a} | |
| 1406 | +\newcommand{\MCprovinceSnoEBsparseRmse}{2.14} | |
| 1407 | +\newcommand{\MCprovinceSnoEBsparseShare}{100} | |
| 1408 | +\newcommand{\MCprovinceSnoEBturningpointBias}{0.001} | |
| 1409 | +\newcommand{\MCprovinceSnoEBturningpointCov}{98} | |
| 1410 | +\newcommand{\MCprovinceSnoEBturningpointLag}{+0.00} | |
| 1411 | +\newcommand{\MCprovinceSnoEBturningpointRev}{n/a} | |
| 1412 | +\newcommand{\MCprovinceSnoEBturningpointRmse}{1.25} | |
| 1413 | +\newcommand{\MCprovinceSnoEBturningpointShare}{100} | |
| 1414 | +\newcommand{\MCprovinceSnoTVbaselineBias}{0.000} | |
| 1415 | +\newcommand{\MCprovinceSnoTVbaselineCov}{98} | |
| 1416 | +\newcommand{\MCprovinceSnoTVbaselineLag}{+0.00} | |
| 1417 | +\newcommand{\MCprovinceSnoTVbaselineRev}{n/a} | |
| 1418 | +\newcommand{\MCprovinceSnoTVbaselineRmse}{1.25} | |
| 1419 | +\newcommand{\MCprovinceSnoTVbaselineShare}{100} | |
| 1420 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsBias}{0.000} | |
| 1421 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsCov}{98} | |
| 1422 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsLag}{+0.00} | |
| 1423 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsRev}{n/a} | |
| 1424 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsRmse}{1.29} | |
| 1425 | +\newcommand{\MCprovinceSnoTVdriftinghedonicsShare}{100} | |
| 1426 | +\newcommand{\MCprovinceSnoTVflatqualityupBias}{0.045} | |
| 1427 | +\newcommand{\MCprovinceSnoTVflatqualityupCov}{97} | |
| 1428 | +\newcommand{\MCprovinceSnoTVflatqualityupLag}{+0.01} | |
| 1429 | +\newcommand{\MCprovinceSnoTVflatqualityupRev}{n/a} | |
| 1430 | +\newcommand{\MCprovinceSnoTVflatqualityupRmse}{1.26} | |
| 1431 | +\newcommand{\MCprovinceSnoTVflatqualityupShare}{100} | |
| 1432 | +\newcommand{\MCprovinceSnoTVheavytailsBias}{-0.005} | |
| 1433 | +\newcommand{\MCprovinceSnoTVheavytailsCov}{98} | |
| 1434 | +\newcommand{\MCprovinceSnoTVheavytailsLag}{+0.00} | |
| 1435 | +\newcommand{\MCprovinceSnoTVheavytailsRev}{n/a} | |
| 1436 | +\newcommand{\MCprovinceSnoTVheavytailsRmse}{1.26} | |
| 1437 | +\newcommand{\MCprovinceSnoTVheavytailsShare}{100} | |
| 1438 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsBias}{-0.001} | |
| 1439 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsCov}{98} | |
| 1440 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsLag}{+0.00} | |
| 1441 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsRev}{n/a} | |
| 1442 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsRmse}{1.37} | |
| 1443 | +\newcommand{\MCprovinceSnoTVidiosyncratictownsShare}{100} | |
| 1444 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsBias}{-0.000} | |
| 1445 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsCov}{98} | |
| 1446 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsLag}{+0.00} | |
| 1447 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsRev}{n/a} | |
| 1448 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsRmse}{1.22} | |
| 1449 | +\newcommand{\MCprovinceSnoTVnolocaldynamicsShare}{100} | |
| 1450 | +\newcommand{\MCprovinceSnoTVrisingqualitydownBias}{-0.043} | |
| 1451 | +\newcommand{\MCprovinceSnoTVrisingqualitydownCov}{97} | |
| 1452 | +\newcommand{\MCprovinceSnoTVrisingqualitydownLag}{+0.00} | |
| 1453 | +\newcommand{\MCprovinceSnoTVrisingqualitydownRev}{n/a} | |
| 1454 | +\newcommand{\MCprovinceSnoTVrisingqualitydownRmse}{1.25} | |
| 1455 | +\newcommand{\MCprovinceSnoTVrisingqualitydownShare}{100} | |
| 1456 | +\newcommand{\MCprovinceSnoTVsparseBias}{-0.003} | |
| 1457 | +\newcommand{\MCprovinceSnoTVsparseCov}{98} | |
| 1458 | +\newcommand{\MCprovinceSnoTVsparseLag}{+0.00} | |
| 1459 | +\newcommand{\MCprovinceSnoTVsparseRev}{n/a} | |
| 1460 | +\newcommand{\MCprovinceSnoTVsparseRmse}{2.13} | |
| 1461 | +\newcommand{\MCprovinceSnoTVsparseShare}{100} | |
| 1462 | +\newcommand{\MCprovinceSnoTVturningpointBias}{0.000} | |
| 1463 | +\newcommand{\MCprovinceSnoTVturningpointCov}{98} | |
| 1464 | +\newcommand{\MCprovinceSnoTVturningpointLag}{+0.00} | |
| 1465 | +\newcommand{\MCprovinceSnoTVturningpointRev}{n/a} | |
| 1466 | +\newcommand{\MCprovinceSnoTVturningpointRmse}{1.25} | |
| 1467 | +\newcommand{\MCprovinceSnoTVturningpointShare}{100} | |
| 1468 | +\newcommand{\MCprovinceSnorobustbaselineBias}{-0.000} | |
| 1469 | +\newcommand{\MCprovinceSnorobustbaselineCov}{98} | |
| 1470 | +\newcommand{\MCprovinceSnorobustbaselineLag}{+0.00} | |
| 1471 | +\newcommand{\MCprovinceSnorobustbaselineRev}{n/a} | |
| 1472 | +\newcommand{\MCprovinceSnorobustbaselineRmse}{1.59} | |
| 1473 | +\newcommand{\MCprovinceSnorobustbaselineShare}{100} | |
| 1474 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsBias}{-0.000} | |
| 1475 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsCov}{98} | |
| 1476 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsLag}{+0.00} | |
| 1477 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsRev}{n/a} | |
| 1478 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsRmse}{1.63} | |
| 1479 | +\newcommand{\MCprovinceSnorobustdriftinghedonicsShare}{100} | |
| 1480 | +\newcommand{\MCprovinceSnorobustflatqualityupBias}{0.049} | |
| 1481 | +\newcommand{\MCprovinceSnorobustflatqualityupCov}{98} | |
| 1482 | +\newcommand{\MCprovinceSnorobustflatqualityupLag}{+0.00} | |
| 1483 | +\newcommand{\MCprovinceSnorobustflatqualityupRev}{n/a} | |
| 1484 | +\newcommand{\MCprovinceSnorobustflatqualityupRmse}{1.59} | |
| 1485 | +\newcommand{\MCprovinceSnorobustflatqualityupShare}{100} | |
| 1486 | +\newcommand{\MCprovinceSnorobustheavytailsBias}{-0.004} | |
| 1487 | +\newcommand{\MCprovinceSnorobustheavytailsCov}{98} | |
| 1488 | +\newcommand{\MCprovinceSnorobustheavytailsLag}{+0.03} | |
| 1489 | +\newcommand{\MCprovinceSnorobustheavytailsRev}{n/a} | |
| 1490 | +\newcommand{\MCprovinceSnorobustheavytailsRmse}{2.25} | |
| 1491 | +\newcommand{\MCprovinceSnorobustheavytailsShare}{100} | |
| 1492 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsBias}{-0.003} | |
| 1493 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsCov}{98} | |
| 1494 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsLag}{+0.00} | |
| 1495 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsRev}{n/a} | |
| 1496 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsRmse}{1.68} | |
| 1497 | +\newcommand{\MCprovinceSnorobustidiosyncratictownsShare}{100} | |
| 1498 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsBias}{-0.001} | |
| 1499 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsCov}{98} | |
| 1500 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsLag}{+0.00} | |
| 1501 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsRev}{n/a} | |
| 1502 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsRmse}{1.57} | |
| 1503 | +\newcommand{\MCprovinceSnorobustnolocaldynamicsShare}{100} | |
| 1504 | +\newcommand{\MCprovinceSnorobustrisingqualitydownBias}{-0.046} | |
| 1505 | +\newcommand{\MCprovinceSnorobustrisingqualitydownCov}{98} | |
| 1506 | +\newcommand{\MCprovinceSnorobustrisingqualitydownLag}{+0.00} | |
| 1507 | +\newcommand{\MCprovinceSnorobustrisingqualitydownRev}{n/a} | |
| 1508 | +\newcommand{\MCprovinceSnorobustrisingqualitydownRmse}{1.59} | |
| 1509 | +\newcommand{\MCprovinceSnorobustrisingqualitydownShare}{100} | |
| 1510 | +\newcommand{\MCprovinceSnorobustsparseBias}{0.002} | |
| 1511 | +\newcommand{\MCprovinceSnorobustsparseCov}{98} | |
| 1512 | +\newcommand{\MCprovinceSnorobustsparseLag}{-0.01} | |
| 1513 | +\newcommand{\MCprovinceSnorobustsparseRev}{n/a} | |
| 1514 | +\newcommand{\MCprovinceSnorobustsparseRmse}{2.62} | |
| 1515 | +\newcommand{\MCprovinceSnorobustsparseShare}{100} | |
| 1516 | +\newcommand{\MCprovinceSnorobustturningpointBias}{-0.000} | |
| 1517 | +\newcommand{\MCprovinceSnorobustturningpointCov}{98} | |
| 1518 | +\newcommand{\MCprovinceSnorobustturningpointLag}{+0.00} | |
| 1519 | +\newcommand{\MCprovinceSnorobustturningpointRev}{n/a} | |
| 1520 | +\newcommand{\MCprovinceSnorobustturningpointRmse}{1.59} | |
| 1521 | +\newcommand{\MCprovinceSnorobustturningpointShare}{100} | |
| 1522 | +\newcommand{\MCprovinceSsharpbaselineBias}{0.001} | |
| 1523 | +\newcommand{\MCprovinceSsharpbaselineCov}{98} | |
| 1524 | +\newcommand{\MCprovinceSsharpbaselineLag}{+0.00} | |
| 1525 | +\newcommand{\MCprovinceSsharpbaselineRev}{n/a} | |
| 1526 | +\newcommand{\MCprovinceSsharpbaselineRmse}{1.25} | |
| 1527 | +\newcommand{\MCprovinceSsharpbaselineShare}{100} | |
| 1528 | +\newcommand{\MCprovinceSsharpbottomupbaselineBias}{0.001} | |
| 1529 | +\newcommand{\MCprovinceSsharpbottomupbaselineCov}{n/a} | |
| 1530 | +\newcommand{\MCprovinceSsharpbottomupbaselineLag}{+0.00} | |
| 1531 | +\newcommand{\MCprovinceSsharpbottomupbaselineRev}{n/a} | |
| 1532 | +\newcommand{\MCprovinceSsharpbottomupbaselineRmse}{1.26} | |
| 1533 | +\newcommand{\MCprovinceSsharpbottomupbaselineShare}{100} | |
| 1534 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsBias}{0.002} | |
| 1535 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsCov}{n/a} | |
| 1536 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsLag}{+0.00} | |
| 1537 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsRev}{n/a} | |
| 1538 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsRmse}{1.31} | |
| 1539 | +\newcommand{\MCprovinceSsharpbottomupdriftinghedonicsShare}{100} | |
| 1540 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupBias}{0.050} | |
| 1541 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupCov}{n/a} | |
| 1542 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupLag}{+0.01} | |
| 1543 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupRev}{n/a} | |
| 1544 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupRmse}{1.27} | |
| 1545 | +\newcommand{\MCprovinceSsharpbottomupflatqualityupShare}{100} | |
| 1546 | +\newcommand{\MCprovinceSsharpbottomupheavytailsBias}{-0.003} | |
| 1547 | +\newcommand{\MCprovinceSsharpbottomupheavytailsCov}{n/a} | |
| 1548 | +\newcommand{\MCprovinceSsharpbottomupheavytailsLag}{-0.03} | |
| 1549 | +\newcommand{\MCprovinceSsharpbottomupheavytailsRev}{n/a} | |
| 1550 | +\newcommand{\MCprovinceSsharpbottomupheavytailsRmse}{1.75} | |
| 1551 | +\newcommand{\MCprovinceSsharpbottomupheavytailsShare}{100} | |
| 1552 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsBias}{-0.006} | |
| 1553 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsCov}{n/a} | |
| 1554 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsLag}{+0.00} | |
| 1555 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsRev}{n/a} | |
| 1556 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsRmse}{1.40} | |
| 1557 | +\newcommand{\MCprovinceSsharpbottomupidiosyncratictownsShare}{100} | |
| 1558 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsBias}{0.001} | |
| 1559 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsCov}{n/a} | |
| 1560 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsLag}{+0.00} | |
| 1561 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsRev}{n/a} | |
| 1562 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsRmse}{1.23} | |
| 1563 | +\newcommand{\MCprovinceSsharpbottomupnolocaldynamicsShare}{100} | |
| 1564 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownBias}{-0.045} | |
| 1565 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownCov}{n/a} | |
| 1566 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownLag}{+0.00} | |
| 1567 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownRev}{n/a} | |
| 1568 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownRmse}{1.26} | |
| 1569 | +\newcommand{\MCprovinceSsharpbottomuprisingqualitydownShare}{100} | |
| 1570 | +\newcommand{\MCprovinceSsharpbottomupsparseBias}{-0.000} | |
| 1571 | +\newcommand{\MCprovinceSsharpbottomupsparseCov}{n/a} | |
| 1572 | +\newcommand{\MCprovinceSsharpbottomupsparseLag}{+0.00} | |
| 1573 | +\newcommand{\MCprovinceSsharpbottomupsparseRev}{n/a} | |
| 1574 | +\newcommand{\MCprovinceSsharpbottomupsparseRmse}{2.15} | |
| 1575 | +\newcommand{\MCprovinceSsharpbottomupsparseShare}{100} | |
| 1576 | +\newcommand{\MCprovinceSsharpbottomupturningpointBias}{0.001} | |
| 1577 | +\newcommand{\MCprovinceSsharpbottomupturningpointCov}{n/a} | |
| 1578 | +\newcommand{\MCprovinceSsharpbottomupturningpointLag}{+0.00} | |
| 1579 | +\newcommand{\MCprovinceSsharpbottomupturningpointRev}{n/a} | |
| 1580 | +\newcommand{\MCprovinceSsharpbottomupturningpointRmse}{1.26} | |
| 1581 | +\newcommand{\MCprovinceSsharpbottomupturningpointShare}{100} | |
| 1582 | +\newcommand{\MCprovinceSsharpdriftinghedonicsBias}{0.001} | |
| 1583 | +\newcommand{\MCprovinceSsharpdriftinghedonicsCov}{98} | |
| 1584 | +\newcommand{\MCprovinceSsharpdriftinghedonicsLag}{+0.00} | |
| 1585 | +\newcommand{\MCprovinceSsharpdriftinghedonicsRev}{n/a} | |
| 1586 | +\newcommand{\MCprovinceSsharpdriftinghedonicsRmse}{1.29} | |
| 1587 | +\newcommand{\MCprovinceSsharpdriftinghedonicsShare}{100} | |
| 1588 | +\newcommand{\MCprovinceSsharpflatqualityupBias}{0.049} | |
| 1589 | +\newcommand{\MCprovinceSsharpflatqualityupCov}{96} | |
| 1590 | +\newcommand{\MCprovinceSsharpflatqualityupLag}{+0.01} | |
| 1591 | +\newcommand{\MCprovinceSsharpflatqualityupRev}{n/a} | |
| 1592 | +\newcommand{\MCprovinceSsharpflatqualityupRmse}{1.25} | |
| 1593 | +\newcommand{\MCprovinceSsharpflatqualityupShare}{100} | |
| 1594 | +\newcommand{\MCprovinceSsharpheavytailsBias}{-0.003} | |
| 1595 | +\newcommand{\MCprovinceSsharpheavytailsCov}{98} | |
| 1596 | +\newcommand{\MCprovinceSsharpheavytailsLag}{+0.00} | |
| 1597 | +\newcommand{\MCprovinceSsharpheavytailsRev}{n/a} | |
| 1598 | +\newcommand{\MCprovinceSsharpheavytailsRmse}{1.26} | |
| 1599 | +\newcommand{\MCprovinceSsharpheavytailsShare}{100} | |
| 1600 | +\newcommand{\MCprovinceSsharpidiosyncratictownsBias}{0.000} | |
| 1601 | +\newcommand{\MCprovinceSsharpidiosyncratictownsCov}{98} | |
| 1602 | +\newcommand{\MCprovinceSsharpidiosyncratictownsLag}{+0.00} | |
| 1603 | +\newcommand{\MCprovinceSsharpidiosyncratictownsRev}{n/a} | |
| 1604 | +\newcommand{\MCprovinceSsharpidiosyncratictownsRmse}{1.37} | |
| 1605 | +\newcommand{\MCprovinceSsharpidiosyncratictownsShare}{100} | |
| 1606 | +\newcommand{\MCprovinceSsharpnolocaldynamicsBias}{0.001} | |
| 1607 | +\newcommand{\MCprovinceSsharpnolocaldynamicsCov}{98} | |
| 1608 | +\newcommand{\MCprovinceSsharpnolocaldynamicsLag}{+0.00} | |
| 1609 | +\newcommand{\MCprovinceSsharpnolocaldynamicsRev}{n/a} | |
| 1610 | +\newcommand{\MCprovinceSsharpnolocaldynamicsRmse}{1.22} | |
| 1611 | +\newcommand{\MCprovinceSsharpnolocaldynamicsShare}{100} | |
| 1612 | +\newcommand{\MCprovinceSsharprisingqualitydownBias}{-0.044} | |
| 1613 | +\newcommand{\MCprovinceSsharprisingqualitydownCov}{97} | |
| 1614 | +\newcommand{\MCprovinceSsharprisingqualitydownLag}{+0.00} | |
| 1615 | +\newcommand{\MCprovinceSsharprisingqualitydownRev}{n/a} | |
| 1616 | +\newcommand{\MCprovinceSsharprisingqualitydownRmse}{1.25} | |
| 1617 | +\newcommand{\MCprovinceSsharprisingqualitydownShare}{100} | |
| 1618 | +\newcommand{\MCprovinceSsharpsparseBias}{-0.002} | |
| 1619 | +\newcommand{\MCprovinceSsharpsparseCov}{98} | |
| 1620 | +\newcommand{\MCprovinceSsharpsparseLag}{+0.00} | |
| 1621 | +\newcommand{\MCprovinceSsharpsparseRev}{n/a} | |
| 1622 | +\newcommand{\MCprovinceSsharpsparseRmse}{2.14} | |
| 1623 | +\newcommand{\MCprovinceSsharpsparseShare}{100} | |
| 1624 | +\newcommand{\MCprovinceSsharpturningpointBias}{0.001} | |
| 1625 | +\newcommand{\MCprovinceSsharpturningpointCov}{98} | |
| 1626 | +\newcommand{\MCprovinceSsharpturningpointLag}{+0.00} | |
| 1627 | +\newcommand{\MCprovinceSsharpturningpointRev}{n/a} | |
| 1628 | +\newcommand{\MCprovinceSsharpturningpointRmse}{1.25} | |
| 1629 | +\newcommand{\MCprovinceSsharpturningpointShare}{100} | |
| 1630 | +\newcommand{\MCregionAmedianbaselineBias}{0.011} | |
| 1631 | +\newcommand{\MCregionAmedianbaselineCov}{n/a} | |
| 1632 | +\newcommand{\MCregionAmedianbaselineLag}{+0.14} | |
| 1633 | +\newcommand{\MCregionAmedianbaselineRev}{n/a} | |
| 1634 | +\newcommand{\MCregionAmedianbaselineRmse}{7.24} | |
| 1635 | +\newcommand{\MCregionAmedianbaselineShare}{100} | |
| 1636 | +\newcommand{\MCregionAmediandriftinghedonicsBias}{0.012} | |
| 1637 | +\newcommand{\MCregionAmediandriftinghedonicsCov}{n/a} | |
| 1638 | +\newcommand{\MCregionAmediandriftinghedonicsLag}{+0.11} | |
| 1639 | +\newcommand{\MCregionAmediandriftinghedonicsRev}{n/a} | |
| 1640 | +\newcommand{\MCregionAmediandriftinghedonicsRmse}{7.56} | |
| 1641 | +\newcommand{\MCregionAmediandriftinghedonicsShare}{100} | |
| 1642 | +\newcommand{\MCregionAmedianflatqualityupBias}{0.267} | |
| 1643 | +\newcommand{\MCregionAmedianflatqualityupCov}{n/a} | |
| 1644 | +\newcommand{\MCregionAmedianflatqualityupLag}{+0.01} | |
| 1645 | +\newcommand{\MCregionAmedianflatqualityupRev}{n/a} | |
| 1646 | +\newcommand{\MCregionAmedianflatqualityupRmse}{7.34} | |
| 1647 | +\newcommand{\MCregionAmedianflatqualityupShare}{100} | |
| 1648 | +\newcommand{\MCregionAmedianheavytailsBias}{0.001} | |
| 1649 | +\newcommand{\MCregionAmedianheavytailsCov}{n/a} | |
| 1650 | +\newcommand{\MCregionAmedianheavytailsLag}{-0.16} | |
| 1651 | +\newcommand{\MCregionAmedianheavytailsRev}{n/a} | |
| 1652 | +\newcommand{\MCregionAmedianheavytailsRmse}{7.34} | |
| 1653 | +\newcommand{\MCregionAmedianheavytailsShare}{100} | |
| 1654 | +\newcommand{\MCregionAmedianidiosyncratictownsBias}{-0.007} | |
| 1655 | +\newcommand{\MCregionAmedianidiosyncratictownsCov}{n/a} | |
| 1656 | +\newcommand{\MCregionAmedianidiosyncratictownsLag}{-0.01} | |
| 1657 | +\newcommand{\MCregionAmedianidiosyncratictownsRev}{n/a} | |
| 1658 | +\newcommand{\MCregionAmedianidiosyncratictownsRmse}{7.49} | |
| 1659 | +\newcommand{\MCregionAmedianidiosyncratictownsShare}{100} | |
| 1660 | +\newcommand{\MCregionAmediannolocaldynamicsBias}{0.009} | |
| 1661 | +\newcommand{\MCregionAmediannolocaldynamicsCov}{n/a} | |
| 1662 | +\newcommand{\MCregionAmediannolocaldynamicsLag}{+0.07} | |
| 1663 | +\newcommand{\MCregionAmediannolocaldynamicsRev}{n/a} | |
| 1664 | +\newcommand{\MCregionAmediannolocaldynamicsRmse}{7.20} | |
| 1665 | +\newcommand{\MCregionAmediannolocaldynamicsShare}{100} | |
| 1666 | +\newcommand{\MCregionAmedianrisingqualitydownBias}{-0.228} | |
| 1667 | +\newcommand{\MCregionAmedianrisingqualitydownCov}{n/a} | |
| 1668 | +\newcommand{\MCregionAmedianrisingqualitydownLag}{+0.07} | |
| 1669 | +\newcommand{\MCregionAmedianrisingqualitydownRev}{n/a} | |
| 1670 | +\newcommand{\MCregionAmedianrisingqualitydownRmse}{7.19} | |
| 1671 | +\newcommand{\MCregionAmedianrisingqualitydownShare}{100} | |
| 1672 | +\newcommand{\MCregionAmediansparseBias}{-0.004} | |
| 1673 | +\newcommand{\MCregionAmediansparseCov}{n/a} | |
| 1674 | +\newcommand{\MCregionAmediansparseLag}{+0.06} | |
| 1675 | +\newcommand{\MCregionAmediansparseRev}{n/a} | |
| 1676 | +\newcommand{\MCregionAmediansparseRmse}{12.37} | |
| 1677 | +\newcommand{\MCregionAmediansparseShare}{100} | |
| 1678 | +\newcommand{\MCregionAmedianturningpointBias}{0.011} | |
| 1679 | +\newcommand{\MCregionAmedianturningpointCov}{n/a} | |
| 1680 | +\newcommand{\MCregionAmedianturningpointLag}{+0.11} | |
| 1681 | +\newcommand{\MCregionAmedianturningpointRev}{n/a} | |
| 1682 | +\newcommand{\MCregionAmedianturningpointRmse}{7.24} | |
| 1683 | +\newcommand{\MCregionAmedianturningpointShare}{100} | |
| 1684 | +\newcommand{\MCregionBstratifiedbaselineBias}{0.010} | |
| 1685 | +\newcommand{\MCregionBstratifiedbaselineCov}{n/a} | |
| 1686 | +\newcommand{\MCregionBstratifiedbaselineLag}{-0.03} | |
| 1687 | +\newcommand{\MCregionBstratifiedbaselineRev}{n/a} | |
| 1688 | +\newcommand{\MCregionBstratifiedbaselineRmse}{6.03} | |
| 1689 | +\newcommand{\MCregionBstratifiedbaselineShare}{100} | |
| 1690 | +\newcommand{\MCregionBstratifieddriftinghedonicsBias}{0.013} | |
| 1691 | +\newcommand{\MCregionBstratifieddriftinghedonicsCov}{n/a} | |
| 1692 | +\newcommand{\MCregionBstratifieddriftinghedonicsLag}{+0.06} | |
| 1693 | +\newcommand{\MCregionBstratifieddriftinghedonicsRev}{n/a} | |
| 1694 | +\newcommand{\MCregionBstratifieddriftinghedonicsRmse}{6.18} | |
| 1695 | +\newcommand{\MCregionBstratifieddriftinghedonicsShare}{100} | |
| 1696 | +\newcommand{\MCregionBstratifiedflatqualityupBias}{0.089} | |
| 1697 | +\newcommand{\MCregionBstratifiedflatqualityupCov}{n/a} | |
| 1698 | +\newcommand{\MCregionBstratifiedflatqualityupLag}{-0.05} | |
| 1699 | +\newcommand{\MCregionBstratifiedflatqualityupRev}{n/a} | |
| 1700 | +\newcommand{\MCregionBstratifiedflatqualityupRmse}{6.12} | |
| 1701 | +\newcommand{\MCregionBstratifiedflatqualityupShare}{100} | |
| 1702 | +\newcommand{\MCregionBstratifiedheavytailsBias}{-0.014} | |
| 1703 | +\newcommand{\MCregionBstratifiedheavytailsCov}{n/a} | |
| 1704 | +\newcommand{\MCregionBstratifiedheavytailsLag}{-0.03} | |
| 1705 | +\newcommand{\MCregionBstratifiedheavytailsRev}{n/a} | |
| 1706 | +\newcommand{\MCregionBstratifiedheavytailsRmse}{6.35} | |
| 1707 | +\newcommand{\MCregionBstratifiedheavytailsShare}{100} | |
| 1708 | +\newcommand{\MCregionBstratifiedidiosyncratictownsBias}{0.004} | |
| 1709 | +\newcommand{\MCregionBstratifiedidiosyncratictownsCov}{n/a} | |
| 1710 | +\newcommand{\MCregionBstratifiedidiosyncratictownsLag}{-0.06} | |
| 1711 | +\newcommand{\MCregionBstratifiedidiosyncratictownsRev}{n/a} | |
| 1712 | +\newcommand{\MCregionBstratifiedidiosyncratictownsRmse}{6.47} | |
| 1713 | +\newcommand{\MCregionBstratifiedidiosyncratictownsShare}{100} | |
| 1714 | +\newcommand{\MCregionBstratifiednolocaldynamicsBias}{0.008} | |
| 1715 | +\newcommand{\MCregionBstratifiednolocaldynamicsCov}{n/a} | |
| 1716 | +\newcommand{\MCregionBstratifiednolocaldynamicsLag}{-0.03} | |
| 1717 | +\newcommand{\MCregionBstratifiednolocaldynamicsRev}{n/a} | |
| 1718 | +\newcommand{\MCregionBstratifiednolocaldynamicsRmse}{6.01} | |
| 1719 | +\newcommand{\MCregionBstratifiednolocaldynamicsShare}{100} | |
| 1720 | +\newcommand{\MCregionBstratifiedrisingqualitydownBias}{-0.058} | |
| 1721 | +\newcommand{\MCregionBstratifiedrisingqualitydownCov}{n/a} | |
| 1722 | +\newcommand{\MCregionBstratifiedrisingqualitydownLag}{+0.00} | |
| 1723 | +\newcommand{\MCregionBstratifiedrisingqualitydownRev}{n/a} | |
| 1724 | +\newcommand{\MCregionBstratifiedrisingqualitydownRmse}{6.09} | |
| 1725 | +\newcommand{\MCregionBstratifiedrisingqualitydownShare}{100} | |
| 1726 | +\newcommand{\MCregionBstratifiedsparseBias}{0.004} | |
| 1727 | +\newcommand{\MCregionBstratifiedsparseCov}{n/a} | |
| 1728 | +\newcommand{\MCregionBstratifiedsparseLag}{+0.01} | |
| 1729 | +\newcommand{\MCregionBstratifiedsparseRev}{n/a} | |
| 1730 | +\newcommand{\MCregionBstratifiedsparseRmse}{9.12} | |
| 1731 | +\newcommand{\MCregionBstratifiedsparseShare}{100} | |
| 1732 | +\newcommand{\MCregionBstratifiedturningpointBias}{0.011} | |
| 1733 | +\newcommand{\MCregionBstratifiedturningpointCov}{n/a} | |
| 1734 | +\newcommand{\MCregionBstratifiedturningpointLag}{-0.00} | |
| 1735 | +\newcommand{\MCregionBstratifiedturningpointRev}{n/a} | |
| 1736 | +\newcommand{\MCregionBstratifiedturningpointRmse}{6.03} | |
| 1737 | +\newcommand{\MCregionBstratifiedturningpointShare}{100} | |
| 1738 | +\newcommand{\MCregionCpooledtdbaselineBias}{0.003} | |
| 1739 | +\newcommand{\MCregionCpooledtdbaselineCov}{98} | |
| 1740 | +\newcommand{\MCregionCpooledtdbaselineLag}{-0.01} | |
| 1741 | +\newcommand{\MCregionCpooledtdbaselineRev}{n/a} | |
| 1742 | +\newcommand{\MCregionCpooledtdbaselineRmse}{2.63} | |
| 1743 | +\newcommand{\MCregionCpooledtdbaselineShare}{100} | |
| 1744 | +\newcommand{\MCregionCpooledtddriftinghedonicsBias}{0.003} | |
| 1745 | +\newcommand{\MCregionCpooledtddriftinghedonicsCov}{98} | |
| 1746 | +\newcommand{\MCregionCpooledtddriftinghedonicsLag}{-0.00} | |
| 1747 | +\newcommand{\MCregionCpooledtddriftinghedonicsRev}{n/a} | |
| 1748 | +\newcommand{\MCregionCpooledtddriftinghedonicsRmse}{2.73} | |
| 1749 | +\newcommand{\MCregionCpooledtddriftinghedonicsShare}{100} | |
| 1750 | +\newcommand{\MCregionCpooledtdflatqualityupBias}{0.047} | |
| 1751 | +\newcommand{\MCregionCpooledtdflatqualityupCov}{98} | |
| 1752 | +\newcommand{\MCregionCpooledtdflatqualityupLag}{-0.03} | |
| 1753 | +\newcommand{\MCregionCpooledtdflatqualityupRev}{n/a} | |
| 1754 | +\newcommand{\MCregionCpooledtdflatqualityupRmse}{2.64} | |
| 1755 | +\newcommand{\MCregionCpooledtdflatqualityupShare}{100} | |
| 1756 | +\newcommand{\MCregionCpooledtdheavytailsBias}{-0.004} | |
| 1757 | +\newcommand{\MCregionCpooledtdheavytailsCov}{98} | |
| 1758 | +\newcommand{\MCregionCpooledtdheavytailsLag}{-0.01} | |
| 1759 | +\newcommand{\MCregionCpooledtdheavytailsRev}{n/a} | |
| 1760 | +\newcommand{\MCregionCpooledtdheavytailsRmse}{2.56} | |
| 1761 | +\newcommand{\MCregionCpooledtdheavytailsShare}{100} | |
| 1762 | +\newcommand{\MCregionCpooledtdidiosyncratictownsBias}{0.002} | |
| 1763 | +\newcommand{\MCregionCpooledtdidiosyncratictownsCov}{98} | |
| 1764 | +\newcommand{\MCregionCpooledtdidiosyncratictownsLag}{-0.01} | |
| 1765 | +\newcommand{\MCregionCpooledtdidiosyncratictownsRev}{n/a} | |
| 1766 | +\newcommand{\MCregionCpooledtdidiosyncratictownsRmse}{2.89} | |
| 1767 | +\newcommand{\MCregionCpooledtdidiosyncratictownsShare}{100} | |
| 1768 | +\newcommand{\MCregionCpooledtdnolocaldynamicsBias}{0.002} | |
| 1769 | +\newcommand{\MCregionCpooledtdnolocaldynamicsCov}{98} | |
| 1770 | +\newcommand{\MCregionCpooledtdnolocaldynamicsLag}{+0.00} | |
| 1771 | +\newcommand{\MCregionCpooledtdnolocaldynamicsRev}{n/a} | |
| 1772 | +\newcommand{\MCregionCpooledtdnolocaldynamicsRmse}{2.61} | |
| 1773 | +\newcommand{\MCregionCpooledtdnolocaldynamicsShare}{100} | |
| 1774 | +\newcommand{\MCregionCpooledtdrisingqualitydownBias}{-0.040} | |
| 1775 | +\newcommand{\MCregionCpooledtdrisingqualitydownCov}{98} | |
| 1776 | +\newcommand{\MCregionCpooledtdrisingqualitydownLag}{-0.02} | |
| 1777 | +\newcommand{\MCregionCpooledtdrisingqualitydownRev}{n/a} | |
| 1778 | +\newcommand{\MCregionCpooledtdrisingqualitydownRmse}{2.63} | |
| 1779 | +\newcommand{\MCregionCpooledtdrisingqualitydownShare}{100} | |
| 1780 | +\newcommand{\MCregionCpooledtdsparseBias}{0.002} | |
| 1781 | +\newcommand{\MCregionCpooledtdsparseCov}{98} | |
| 1782 | +\newcommand{\MCregionCpooledtdsparseLag}{+0.04} | |
| 1783 | +\newcommand{\MCregionCpooledtdsparseRev}{n/a} | |
| 1784 | +\newcommand{\MCregionCpooledtdsparseRmse}{4.64} | |
| 1785 | +\newcommand{\MCregionCpooledtdsparseShare}{100} | |
| 1786 | +\newcommand{\MCregionCpooledtdturningpointBias}{0.003} | |
| 1787 | +\newcommand{\MCregionCpooledtdturningpointCov}{98} | |
| 1788 | +\newcommand{\MCregionCpooledtdturningpointLag}{-0.01} | |
| 1789 | +\newcommand{\MCregionCpooledtdturningpointRev}{n/a} | |
| 1790 | +\newcommand{\MCregionCpooledtdturningpointRmse}{2.63} | |
| 1791 | +\newcommand{\MCregionCpooledtdturningpointShare}{100} | |
| 1792 | +\newcommand{\MCregionDrtdOneThreebaselineBias}{0.000} | |
| 1793 | +\newcommand{\MCregionDrtdOneThreebaselineCov}{n/a} | |
| 1794 | +\newcommand{\MCregionDrtdOneThreebaselineLag}{-0.03} | |
| 1795 | +\newcommand{\MCregionDrtdOneThreebaselineRev}{n/a} | |
| 1796 | +\newcommand{\MCregionDrtdOneThreebaselineRmse}{2.99} | |
| 1797 | +\newcommand{\MCregionDrtdOneThreebaselineShare}{100} | |
| 1798 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsBias}{0.002} | |
| 1799 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsCov}{n/a} | |
| 1800 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsLag}{-0.03} | |
| 1801 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsRev}{n/a} | |
| 1802 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsRmse}{3.10} | |
| 1803 | +\newcommand{\MCregionDrtdOneThreedriftinghedonicsShare}{100} | |
| 1804 | +\newcommand{\MCregionDrtdOneThreeflatqualityupBias}{0.045} | |
| 1805 | +\newcommand{\MCregionDrtdOneThreeflatqualityupCov}{n/a} | |
| 1806 | +\newcommand{\MCregionDrtdOneThreeflatqualityupLag}{-0.04} | |
| 1807 | +\newcommand{\MCregionDrtdOneThreeflatqualityupRev}{n/a} | |
| 1808 | +\newcommand{\MCregionDrtdOneThreeflatqualityupRmse}{3.00} | |
| 1809 | +\newcommand{\MCregionDrtdOneThreeflatqualityupShare}{100} | |
| 1810 | +\newcommand{\MCregionDrtdOneThreeheavytailsBias}{-0.005} | |
| 1811 | +\newcommand{\MCregionDrtdOneThreeheavytailsCov}{n/a} | |
| 1812 | +\newcommand{\MCregionDrtdOneThreeheavytailsLag}{-0.02} | |
| 1813 | +\newcommand{\MCregionDrtdOneThreeheavytailsRev}{n/a} | |
| 1814 | +\newcommand{\MCregionDrtdOneThreeheavytailsRmse}{2.99} | |
| 1815 | +\newcommand{\MCregionDrtdOneThreeheavytailsShare}{100} | |
| 1816 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsBias}{-0.001} | |
| 1817 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsCov}{n/a} | |
| 1818 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsLag}{+0.00} | |
| 1819 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsRev}{n/a} | |
| 1820 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsRmse}{3.11} | |
| 1821 | +\newcommand{\MCregionDrtdOneThreeidiosyncratictownsShare}{100} | |
| 1822 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsBias}{-0.000} | |
| 1823 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsCov}{n/a} | |
| 1824 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsLag}{-0.02} | |
| 1825 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsRev}{n/a} | |
| 1826 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsRmse}{2.99} | |
| 1827 | +\newcommand{\MCregionDrtdOneThreenolocaldynamicsShare}{100} | |
| 1828 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownBias}{-0.042} | |
| 1829 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownCov}{n/a} | |
| 1830 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownLag}{-0.03} | |
| 1831 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownRev}{n/a} | |
| 1832 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownRmse}{2.99} | |
| 1833 | +\newcommand{\MCregionDrtdOneThreerisingqualitydownShare}{100} | |
| 1834 | +\newcommand{\MCregionDrtdOneThreesparseBias}{-0.000} | |
| 1835 | +\newcommand{\MCregionDrtdOneThreesparseCov}{n/a} | |
| 1836 | +\newcommand{\MCregionDrtdOneThreesparseLag}{+0.01} | |
| 1837 | +\newcommand{\MCregionDrtdOneThreesparseRev}{n/a} | |
| 1838 | +\newcommand{\MCregionDrtdOneThreesparseRmse}{5.45} | |
| 1839 | +\newcommand{\MCregionDrtdOneThreesparseShare}{100} | |
| 1840 | +\newcommand{\MCregionDrtdOneThreeturningpointBias}{0.000} | |
| 1841 | +\newcommand{\MCregionDrtdOneThreeturningpointCov}{n/a} | |
| 1842 | +\newcommand{\MCregionDrtdOneThreeturningpointLag}{-0.01} | |
| 1843 | +\newcommand{\MCregionDrtdOneThreeturningpointRev}{n/a} | |
| 1844 | +\newcommand{\MCregionDrtdOneThreeturningpointRmse}{2.99} | |
| 1845 | +\newcommand{\MCregionDrtdOneThreeturningpointShare}{100} | |
| 1846 | +\newcommand{\MCregionHrepeatsalesbaselineBias}{0.006} | |
| 1847 | +\newcommand{\MCregionHrepeatsalesbaselineCov}{n/a} | |
| 1848 | +\newcommand{\MCregionHrepeatsalesbaselineLag}{-0.05} | |
| 1849 | +\newcommand{\MCregionHrepeatsalesbaselineRev}{n/a} | |
| 1850 | +\newcommand{\MCregionHrepeatsalesbaselineRmse}{12.03} | |
| 1851 | +\newcommand{\MCregionHrepeatsalesbaselineShare}{100} | |
| 1852 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsBias}{0.007} | |
| 1853 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsCov}{n/a} | |
| 1854 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsLag}{-0.04} | |
| 1855 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsRev}{n/a} | |
| 1856 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsRmse}{12.18} | |
| 1857 | +\newcommand{\MCregionHrepeatsalesdriftinghedonicsShare}{100} | |
| 1858 | +\newcommand{\MCregionHrepeatsalesflatqualityupBias}{0.053} | |
| 1859 | +\newcommand{\MCregionHrepeatsalesflatqualityupCov}{n/a} | |
| 1860 | +\newcommand{\MCregionHrepeatsalesflatqualityupLag}{+0.03} | |
| 1861 | +\newcommand{\MCregionHrepeatsalesflatqualityupRev}{n/a} | |
| 1862 | +\newcommand{\MCregionHrepeatsalesflatqualityupRmse}{12.05} | |
| 1863 | +\newcommand{\MCregionHrepeatsalesflatqualityupShare}{100} | |
| 1864 | +\newcommand{\MCregionHrepeatsalesheavytailsBias}{-0.031} | |
| 1865 | +\newcommand{\MCregionHrepeatsalesheavytailsCov}{n/a} | |
| 1866 | +\newcommand{\MCregionHrepeatsalesheavytailsLag}{+0.12} | |
| 1867 | +\newcommand{\MCregionHrepeatsalesheavytailsRev}{n/a} | |
| 1868 | +\newcommand{\MCregionHrepeatsalesheavytailsRmse}{15.80} | |
| 1869 | +\newcommand{\MCregionHrepeatsalesheavytailsShare}{100} | |
| 1870 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsBias}{0.009} | |
| 1871 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsCov}{n/a} | |
| 1872 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsLag}{+0.01} | |
| 1873 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsRev}{n/a} | |
| 1874 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsRmse}{12.59} | |
| 1875 | +\newcommand{\MCregionHrepeatsalesidiosyncratictownsShare}{100} | |
| 1876 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsBias}{0.005} | |
| 1877 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsCov}{n/a} | |
| 1878 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsLag}{-0.02} | |
| 1879 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsRev}{n/a} | |
| 1880 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsRmse}{12.00} | |
| 1881 | +\newcommand{\MCregionHrepeatsalesnolocaldynamicsShare}{100} | |
| 1882 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownBias}{-0.026} | |
| 1883 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownCov}{n/a} | |
| 1884 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownLag}{-0.05} | |
| 1885 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownRev}{n/a} | |
| 1886 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownRmse}{12.04} | |
| 1887 | +\newcommand{\MCregionHrepeatsalesrisingqualitydownShare}{100} | |
| 1888 | +\newcommand{\MCregionHrepeatsalessparseBias}{-0.023} | |
| 1889 | +\newcommand{\MCregionHrepeatsalessparseCov}{n/a} | |
| 1890 | +\newcommand{\MCregionHrepeatsalessparseLag}{-0.06} | |
| 1891 | +\newcommand{\MCregionHrepeatsalessparseRev}{n/a} | |
| 1892 | +\newcommand{\MCregionHrepeatsalessparseRmse}{22.22} | |
| 1893 | +\newcommand{\MCregionHrepeatsalessparseShare}{97} | |
| 1894 | +\newcommand{\MCregionHrepeatsalesturningpointBias}{0.006} | |
| 1895 | +\newcommand{\MCregionHrepeatsalesturningpointCov}{n/a} | |
| 1896 | +\newcommand{\MCregionHrepeatsalesturningpointLag}{+0.01} | |
| 1897 | +\newcommand{\MCregionHrepeatsalesturningpointRev}{n/a} | |
| 1898 | +\newcommand{\MCregionHrepeatsalesturningpointRmse}{12.03} | |
| 1899 | +\newcommand{\MCregionHrepeatsalesturningpointShare}{100} | |
| 1900 | +\newcommand{\MCregionKqhpibaselineBias}{-0.006} | |
| 1901 | +\newcommand{\MCregionKqhpibaselineCov}{94} | |
| 1902 | +\newcommand{\MCregionKqhpibaselineLag}{-0.02} | |
| 1903 | +\newcommand{\MCregionKqhpibaselineRev}{n/a} | |
| 1904 | +\newcommand{\MCregionKqhpibaselineRmse}{1.17} | |
| 1905 | +\newcommand{\MCregionKqhpibaselineShare}{100} | |
| 1906 | +\newcommand{\MCregionKqhpidriftinghedonicsBias}{-0.007} | |
| 1907 | +\newcommand{\MCregionKqhpidriftinghedonicsCov}{93} | |
| 1908 | +\newcommand{\MCregionKqhpidriftinghedonicsLag}{+0.00} | |
| 1909 | +\newcommand{\MCregionKqhpidriftinghedonicsRev}{n/a} | |
| 1910 | +\newcommand{\MCregionKqhpidriftinghedonicsRmse}{1.19} | |
| 1911 | +\newcommand{\MCregionKqhpidriftinghedonicsShare}{100} | |
| 1912 | +\newcommand{\MCregionKqhpiflatqualityupBias}{0.046} | |
| 1913 | +\newcommand{\MCregionKqhpiflatqualityupCov}{83} | |
| 1914 | +\newcommand{\MCregionKqhpiflatqualityupLag}{-0.03} | |
| 1915 | +\newcommand{\MCregionKqhpiflatqualityupRev}{n/a} | |
| 1916 | +\newcommand{\MCregionKqhpiflatqualityupRmse}{0.84} | |
| 1917 | +\newcommand{\MCregionKqhpiflatqualityupShare}{100} | |
| 1918 | +\newcommand{\MCregionKqhpiheavytailsBias}{-0.010} | |
| 1919 | +\newcommand{\MCregionKqhpiheavytailsCov}{92} | |
| 1920 | +\newcommand{\MCregionKqhpiheavytailsLag}{-0.01} | |
| 1921 | +\newcommand{\MCregionKqhpiheavytailsRev}{n/a} | |
| 1922 | +\newcommand{\MCregionKqhpiheavytailsRmse}{1.21} | |
| 1923 | +\newcommand{\MCregionKqhpiheavytailsShare}{100} | |
| 1924 | +\newcommand{\MCregionKqhpiidiosyncratictownsBias}{-0.007} | |
| 1925 | +\newcommand{\MCregionKqhpiidiosyncratictownsCov}{92} | |
| 1926 | +\newcommand{\MCregionKqhpiidiosyncratictownsLag}{+0.01} | |
| 1927 | +\newcommand{\MCregionKqhpiidiosyncratictownsRev}{n/a} | |
| 1928 | +\newcommand{\MCregionKqhpiidiosyncratictownsRmse}{1.27} | |
| 1929 | +\newcommand{\MCregionKqhpiidiosyncratictownsShare}{100} | |
| 1930 | +\newcommand{\MCregionKqhpinolocaldynamicsBias}{-0.008} | |
| 1931 | +\newcommand{\MCregionKqhpinolocaldynamicsCov}{94} | |
| 1932 | +\newcommand{\MCregionKqhpinolocaldynamicsLag}{-0.02} | |
| 1933 | +\newcommand{\MCregionKqhpinolocaldynamicsRev}{n/a} | |
| 1934 | +\newcommand{\MCregionKqhpinolocaldynamicsRmse}{1.08} | |
| 1935 | +\newcommand{\MCregionKqhpinolocaldynamicsShare}{100} | |
| 1936 | +\newcommand{\MCregionKqhpirisingqualitydownBias}{-0.055} | |
| 1937 | +\newcommand{\MCregionKqhpirisingqualitydownCov}{88} | |
| 1938 | +\newcommand{\MCregionKqhpirisingqualitydownLag}{-0.03} | |
| 1939 | +\newcommand{\MCregionKqhpirisingqualitydownRev}{n/a} | |
| 1940 | +\newcommand{\MCregionKqhpirisingqualitydownRmse}{1.25} | |
| 1941 | +\newcommand{\MCregionKqhpirisingqualitydownShare}{100} | |
| 1942 | +\newcommand{\MCregionKqhpisparseBias}{-0.010} | |
| 1943 | +\newcommand{\MCregionKqhpisparseCov}{79} | |
| 1944 | +\newcommand{\MCregionKqhpisparseLag}{+0.03} | |
| 1945 | +\newcommand{\MCregionKqhpisparseRev}{n/a} | |
| 1946 | +\newcommand{\MCregionKqhpisparseRmse}{2.14} | |
| 1947 | +\newcommand{\MCregionKqhpisparseShare}{100} | |
| 1948 | +\newcommand{\MCregionKqhpiturningpointBias}{0.007} | |
| 1949 | +\newcommand{\MCregionKqhpiturningpointCov}{94} | |
| 1950 | +\newcommand{\MCregionKqhpiturningpointLag}{-0.00} | |
| 1951 | +\newcommand{\MCregionKqhpiturningpointRev}{n/a} | |
| 1952 | +\newcommand{\MCregionKqhpiturningpointRmse}{1.23} | |
| 1953 | +\newcommand{\MCregionKqhpiturningpointShare}{100} | |
| 1954 | +\newcommand{\MCregionSLLTbaselineBias}{0.002} | |
| 1955 | +\newcommand{\MCregionSLLTbaselineCov}{98} | |
| 1956 | +\newcommand{\MCregionSLLTbaselineLag}{+0.01} | |
| 1957 | +\newcommand{\MCregionSLLTbaselineRev}{0.82} | |
| 1958 | +\newcommand{\MCregionSLLTbaselineRmse}{1.39} | |
| 1959 | +\newcommand{\MCregionSLLTbaselineShare}{100} | |
| 1960 | +\newcommand{\MCregionSLLTdriftinghedonicsBias}{0.003} | |
| 1961 | +\newcommand{\MCregionSLLTdriftinghedonicsCov}{98} | |
| 1962 | +\newcommand{\MCregionSLLTdriftinghedonicsLag}{+0.01} | |
| 1963 | +\newcommand{\MCregionSLLTdriftinghedonicsRev}{0.82} | |
| 1964 | +\newcommand{\MCregionSLLTdriftinghedonicsRmse}{1.43} | |
| 1965 | +\newcommand{\MCregionSLLTdriftinghedonicsShare}{100} | |
| 1966 | +\newcommand{\MCregionSLLTflatqualityupBias}{0.051} | |
| 1967 | +\newcommand{\MCregionSLLTflatqualityupCov}{96} | |
| 1968 | +\newcommand{\MCregionSLLTflatqualityupLag}{+0.02} | |
| 1969 | +\newcommand{\MCregionSLLTflatqualityupRev}{0.82} | |
| 1970 | +\newcommand{\MCregionSLLTflatqualityupRmse}{1.40} | |
| 1971 | +\newcommand{\MCregionSLLTflatqualityupShare}{100} | |
| 1972 | +\newcommand{\MCregionSLLTheavytailsBias}{-0.005} | |
| 1973 | +\newcommand{\MCregionSLLTheavytailsCov}{93} | |
| 1974 | +\newcommand{\MCregionSLLTheavytailsLag}{-0.03} | |
| 1975 | +\newcommand{\MCregionSLLTheavytailsRev}{1.11} | |
| 1976 | +\newcommand{\MCregionSLLTheavytailsRmse}{2.34} | |
| 1977 | +\newcommand{\MCregionSLLTheavytailsShare}{100} | |
| 1978 | +\newcommand{\MCregionSLLTidiosyncratictownsBias}{-0.002} | |
| 1979 | +\newcommand{\MCregionSLLTidiosyncratictownsCov}{98} | |
| 1980 | +\newcommand{\MCregionSLLTidiosyncratictownsLag}{+0.00} | |
| 1981 | +\newcommand{\MCregionSLLTidiosyncratictownsRev}{0.91} | |
| 1982 | +\newcommand{\MCregionSLLTidiosyncratictownsRmse}{1.57} | |
| 1983 | +\newcommand{\MCregionSLLTidiosyncratictownsShare}{100} | |
| 1984 | +\newcommand{\MCregionSLLTnolocaldynamicsBias}{0.001} | |
| 1985 | +\newcommand{\MCregionSLLTnolocaldynamicsCov}{99} | |
| 1986 | +\newcommand{\MCregionSLLTnolocaldynamicsLag}{+0.00} | |
| 1987 | +\newcommand{\MCregionSLLTnolocaldynamicsRev}{0.64} | |
| 1988 | +\newcommand{\MCregionSLLTnolocaldynamicsRmse}{1.23} | |
| 1989 | +\newcommand{\MCregionSLLTnolocaldynamicsShare}{100} | |
| 1990 | +\newcommand{\MCregionSLLTrisingqualitydownBias}{-0.044} | |
| 1991 | +\newcommand{\MCregionSLLTrisingqualitydownCov}{96} | |
| 1992 | +\newcommand{\MCregionSLLTrisingqualitydownLag}{+0.00} | |
| 1993 | +\newcommand{\MCregionSLLTrisingqualitydownRev}{0.82} | |
| 1994 | +\newcommand{\MCregionSLLTrisingqualitydownRmse}{1.39} | |
| 1995 | +\newcommand{\MCregionSLLTrisingqualitydownShare}{100} | |
| 1996 | +\newcommand{\MCregionSLLTsparseBias}{0.002} | |
| 1997 | +\newcommand{\MCregionSLLTsparseCov}{99} | |
| 1998 | +\newcommand{\MCregionSLLTsparseLag}{+0.00} | |
| 1999 | +\newcommand{\MCregionSLLTsparseRev}{1.24} | |
| 2000 | +\newcommand{\MCregionSLLTsparseRmse}{2.23} | |
| 2001 | +\newcommand{\MCregionSLLTsparseShare}{100} | |
| 2002 | +\newcommand{\MCregionSLLTturningpointBias}{0.002} | |
| 2003 | +\newcommand{\MCregionSLLTturningpointCov}{98} | |
| 2004 | +\newcommand{\MCregionSLLTturningpointLag}{+0.01} | |
| 2005 | +\newcommand{\MCregionSLLTturningpointRev}{0.82} | |
| 2006 | +\newcommand{\MCregionSLLTturningpointRmse}{1.39} | |
| 2007 | +\newcommand{\MCregionSLLTturningpointShare}{100} | |
| 2008 | +\newcommand{\MCregionScoarselocbaselineBias}{0.010} | |
| 2009 | +\newcommand{\MCregionScoarselocbaselineCov}{99} | |
| 2010 | +\newcommand{\MCregionScoarselocbaselineLag}{+0.00} | |
| 2011 | +\newcommand{\MCregionScoarselocbaselineRev}{0.61} | |
| 2012 | +\newcommand{\MCregionScoarselocbaselineRmse}{1.97} | |
| 2013 | +\newcommand{\MCregionScoarselocbaselineShare}{100} | |
| 2014 | +\newcommand{\MCregionScoarselocdriftinghedonicsBias}{0.011} | |
| 2015 | +\newcommand{\MCregionScoarselocdriftinghedonicsCov}{99} | |
| 2016 | +\newcommand{\MCregionScoarselocdriftinghedonicsLag}{+0.02} | |
| 2017 | +\newcommand{\MCregionScoarselocdriftinghedonicsRev}{0.61} | |
| 2018 | +\newcommand{\MCregionScoarselocdriftinghedonicsRmse}{2.01} | |
| 2019 | +\newcommand{\MCregionScoarselocdriftinghedonicsShare}{100} | |
| 2020 | +\newcommand{\MCregionScoarselocflatqualityupBias}{0.046} | |
| 2021 | +\newcommand{\MCregionScoarselocflatqualityupCov}{98} | |
| 2022 | +\newcommand{\MCregionScoarselocflatqualityupLag}{+0.07} | |
| 2023 | +\newcommand{\MCregionScoarselocflatqualityupRev}{0.61} | |
| 2024 | +\newcommand{\MCregionScoarselocflatqualityupRmse}{1.97} | |
| 2025 | +\newcommand{\MCregionScoarselocflatqualityupShare}{100} | |
| 2026 | +\newcommand{\MCregionScoarselocheavytailsBias}{0.001} | |
| 2027 | +\newcommand{\MCregionScoarselocheavytailsCov}{99} | |
| 2028 | +\newcommand{\MCregionScoarselocheavytailsLag}{+0.03} | |
| 2029 | +\newcommand{\MCregionScoarselocheavytailsRev}{0.87} | |
| 2030 | +\newcommand{\MCregionScoarselocheavytailsRmse}{2.18} | |
| 2031 | +\newcommand{\MCregionScoarselocheavytailsShare}{100} | |
| 2032 | +\newcommand{\MCregionScoarselocidiosyncratictownsBias}{-0.004} | |
| 2033 | +\newcommand{\MCregionScoarselocidiosyncratictownsCov}{99} | |
| 2034 | +\newcommand{\MCregionScoarselocidiosyncratictownsLag}{+0.01} | |
| 2035 | +\newcommand{\MCregionScoarselocidiosyncratictownsRev}{0.69} | |
| 2036 | +\newcommand{\MCregionScoarselocidiosyncratictownsRmse}{2.11} | |
| 2037 | +\newcommand{\MCregionScoarselocidiosyncratictownsShare}{100} | |
| 2038 | +\newcommand{\MCregionScoarselocnolocaldynamicsBias}{0.008} | |
| 2039 | +\newcommand{\MCregionScoarselocnolocaldynamicsCov}{99} | |
| 2040 | +\newcommand{\MCregionScoarselocnolocaldynamicsLag}{+0.01} | |
| 2041 | +\newcommand{\MCregionScoarselocnolocaldynamicsRev}{0.40} | |
| 2042 | +\newcommand{\MCregionScoarselocnolocaldynamicsRmse}{1.87} | |
| 2043 | +\newcommand{\MCregionScoarselocnolocaldynamicsShare}{100} | |
| 2044 | +\newcommand{\MCregionScoarselocrisingqualitydownBias}{-0.023} | |
| 2045 | +\newcommand{\MCregionScoarselocrisingqualitydownCov}{98} | |
| 2046 | +\newcommand{\MCregionScoarselocrisingqualitydownLag}{+0.00} | |
| 2047 | +\newcommand{\MCregionScoarselocrisingqualitydownRev}{0.61} | |
| 2048 | +\newcommand{\MCregionScoarselocrisingqualitydownRmse}{1.97} | |
| 2049 | +\newcommand{\MCregionScoarselocrisingqualitydownShare}{100} | |
| 2050 | +\newcommand{\MCregionScoarselocsparseBias}{-0.007} | |
| 2051 | +\newcommand{\MCregionScoarselocsparseCov}{99} | |
| 2052 | +\newcommand{\MCregionScoarselocsparseLag}{+0.12} | |
| 2053 | +\newcommand{\MCregionScoarselocsparseRev}{0.86} | |
| 2054 | +\newcommand{\MCregionScoarselocsparseRmse}{3.24} | |
| 2055 | +\newcommand{\MCregionScoarselocsparseShare}{100} | |
| 2056 | +\newcommand{\MCregionScoarselocturningpointBias}{0.010} | |
| 2057 | +\newcommand{\MCregionScoarselocturningpointCov}{99} | |
| 2058 | +\newcommand{\MCregionScoarselocturningpointLag}{+0.01} | |
| 2059 | +\newcommand{\MCregionScoarselocturningpointRev}{0.61} | |
| 2060 | +\newcommand{\MCregionScoarselocturningpointRmse}{1.97} | |
| 2061 | +\newcommand{\MCregionScoarselocturningpointShare}{100} | |
| 2062 | +\newcommand{\MCregionSnoEBbaselineBias}{0.002} | |
| 2063 | +\newcommand{\MCregionSnoEBbaselineCov}{98} | |
| 2064 | +\newcommand{\MCregionSnoEBbaselineLag}{+0.00} | |
| 2065 | +\newcommand{\MCregionSnoEBbaselineRev}{0.53} | |
| 2066 | +\newcommand{\MCregionSnoEBbaselineRmse}{1.39} | |
| 2067 | +\newcommand{\MCregionSnoEBbaselineShare}{100} | |
| 2068 | +\newcommand{\MCregionSnoEBdriftinghedonicsBias}{0.002} | |
| 2069 | +\newcommand{\MCregionSnoEBdriftinghedonicsCov}{98} | |
| 2070 | +\newcommand{\MCregionSnoEBdriftinghedonicsLag}{+0.01} | |
| 2071 | +\newcommand{\MCregionSnoEBdriftinghedonicsRev}{0.53} | |
| 2072 | +\newcommand{\MCregionSnoEBdriftinghedonicsRmse}{1.43} | |
| 2073 | +\newcommand{\MCregionSnoEBdriftinghedonicsShare}{100} | |
| 2074 | +\newcommand{\MCregionSnoEBflatqualityupBias}{0.051} | |
| 2075 | +\newcommand{\MCregionSnoEBflatqualityupCov}{96} | |
| 2076 | +\newcommand{\MCregionSnoEBflatqualityupLag}{+0.03} | |
| 2077 | +\newcommand{\MCregionSnoEBflatqualityupRev}{0.53} | |
| 2078 | +\newcommand{\MCregionSnoEBflatqualityupRmse}{1.40} | |
| 2079 | +\newcommand{\MCregionSnoEBflatqualityupShare}{100} | |
| 2080 | +\newcommand{\MCregionSnoEBheavytailsBias}{-0.005} | |
| 2081 | +\newcommand{\MCregionSnoEBheavytailsCov}{94} | |
| 2082 | +\newcommand{\MCregionSnoEBheavytailsLag}{-0.02} | |
| 2083 | +\newcommand{\MCregionSnoEBheavytailsRev}{0.89} | |
| 2084 | +\newcommand{\MCregionSnoEBheavytailsRmse}{2.16} | |
| 2085 | +\newcommand{\MCregionSnoEBheavytailsShare}{100} | |
| 2086 | +\newcommand{\MCregionSnoEBidiosyncratictownsBias}{-0.001} | |
| 2087 | +\newcommand{\MCregionSnoEBidiosyncratictownsCov}{98} | |
| 2088 | +\newcommand{\MCregionSnoEBidiosyncratictownsLag}{+0.00} | |
| 2089 | +\newcommand{\MCregionSnoEBidiosyncratictownsRev}{0.61} | |
| 2090 | +\newcommand{\MCregionSnoEBidiosyncratictownsRmse}{1.58} | |
| 2091 | +\newcommand{\MCregionSnoEBidiosyncratictownsShare}{100} | |
| 2092 | +\newcommand{\MCregionSnoEBnolocaldynamicsBias}{0.000} | |
| 2093 | +\newcommand{\MCregionSnoEBnolocaldynamicsCov}{99} | |
| 2094 | +\newcommand{\MCregionSnoEBnolocaldynamicsLag}{+0.00} | |
| 2095 | +\newcommand{\MCregionSnoEBnolocaldynamicsRev}{0.32} | |
| 2096 | +\newcommand{\MCregionSnoEBnolocaldynamicsRmse}{1.24} | |
| 2097 | +\newcommand{\MCregionSnoEBnolocaldynamicsShare}{100} | |
| 2098 | +\newcommand{\MCregionSnoEBrisingqualitydownBias}{-0.044} | |
| 2099 | +\newcommand{\MCregionSnoEBrisingqualitydownCov}{96} | |
| 2100 | +\newcommand{\MCregionSnoEBrisingqualitydownLag}{+0.00} | |
| 2101 | +\newcommand{\MCregionSnoEBrisingqualitydownRev}{0.53} | |
| 2102 | +\newcommand{\MCregionSnoEBrisingqualitydownRmse}{1.39} | |
| 2103 | +\newcommand{\MCregionSnoEBrisingqualitydownShare}{100} | |
| 2104 | +\newcommand{\MCregionSnoEBsparseBias}{0.001} | |
| 2105 | +\newcommand{\MCregionSnoEBsparseCov}{99} | |
| 2106 | +\newcommand{\MCregionSnoEBsparseLag}{+0.01} | |
| 2107 | +\newcommand{\MCregionSnoEBsparseRev}{0.73} | |
| 2108 | +\newcommand{\MCregionSnoEBsparseRmse}{2.24} | |
| 2109 | +\newcommand{\MCregionSnoEBsparseShare}{100} | |
| 2110 | +\newcommand{\MCregionSnoEBturningpointBias}{0.002} | |
| 2111 | +\newcommand{\MCregionSnoEBturningpointCov}{98} | |
| 2112 | +\newcommand{\MCregionSnoEBturningpointLag}{+0.01} | |
| 2113 | +\newcommand{\MCregionSnoEBturningpointRev}{0.53} | |
| 2114 | +\newcommand{\MCregionSnoEBturningpointRmse}{1.39} | |
| 2115 | +\newcommand{\MCregionSnoEBturningpointShare}{100} | |
| 2116 | +\newcommand{\MCregionSnoTVbaselineBias}{0.000} | |
| 2117 | +\newcommand{\MCregionSnoTVbaselineCov}{99} | |
| 2118 | +\newcommand{\MCregionSnoTVbaselineLag}{+0.00} | |
| 2119 | +\newcommand{\MCregionSnoTVbaselineRev}{0.59} | |
| 2120 | +\newcommand{\MCregionSnoTVbaselineRmse}{1.40} | |
| 2121 | +\newcommand{\MCregionSnoTVbaselineShare}{100} | |
| 2122 | +\newcommand{\MCregionSnoTVdriftinghedonicsBias}{0.001} | |
| 2123 | +\newcommand{\MCregionSnoTVdriftinghedonicsCov}{99} | |
| 2124 | +\newcommand{\MCregionSnoTVdriftinghedonicsLag}{+0.00} | |
| 2125 | +\newcommand{\MCregionSnoTVdriftinghedonicsRev}{0.59} | |
| 2126 | +\newcommand{\MCregionSnoTVdriftinghedonicsRmse}{1.44} | |
| 2127 | +\newcommand{\MCregionSnoTVdriftinghedonicsShare}{100} | |
| 2128 | +\newcommand{\MCregionSnoTVflatqualityupBias}{0.047} | |
| 2129 | +\newcommand{\MCregionSnoTVflatqualityupCov}{97} | |
| 2130 | +\newcommand{\MCregionSnoTVflatqualityupLag}{+0.02} | |
| 2131 | +\newcommand{\MCregionSnoTVflatqualityupRev}{0.59} | |
| 2132 | +\newcommand{\MCregionSnoTVflatqualityupRmse}{1.41} | |
| 2133 | +\newcommand{\MCregionSnoTVflatqualityupShare}{100} | |
| 2134 | +\newcommand{\MCregionSnoTVheavytailsBias}{-0.006} | |
| 2135 | +\newcommand{\MCregionSnoTVheavytailsCov}{94} | |
| 2136 | +\newcommand{\MCregionSnoTVheavytailsLag}{-0.03} | |
| 2137 | +\newcommand{\MCregionSnoTVheavytailsRev}{0.90} | |
| 2138 | +\newcommand{\MCregionSnoTVheavytailsRmse}{2.32} | |
| 2139 | +\newcommand{\MCregionSnoTVheavytailsShare}{100} | |
| 2140 | +\newcommand{\MCregionSnoTVidiosyncratictownsBias}{-0.003} | |
| 2141 | +\newcommand{\MCregionSnoTVidiosyncratictownsCov}{98} | |
| 2142 | +\newcommand{\MCregionSnoTVidiosyncratictownsLag}{+0.00} | |
| 2143 | +\newcommand{\MCregionSnoTVidiosyncratictownsRev}{0.65} | |
| 2144 | +\newcommand{\MCregionSnoTVidiosyncratictownsRmse}{1.58} | |
| 2145 | +\newcommand{\MCregionSnoTVidiosyncratictownsShare}{100} | |
| 2146 | +\newcommand{\MCregionSnoTVnolocaldynamicsBias}{-0.001} | |
| 2147 | +\newcommand{\MCregionSnoTVnolocaldynamicsCov}{99} | |
| 2148 | +\newcommand{\MCregionSnoTVnolocaldynamicsLag}{+0.00} | |
| 2149 | +\newcommand{\MCregionSnoTVnolocaldynamicsRev}{0.33} | |
| 2150 | +\newcommand{\MCregionSnoTVnolocaldynamicsRmse}{1.23} | |
| 2151 | +\newcommand{\MCregionSnoTVnolocaldynamicsShare}{100} | |
| 2152 | +\newcommand{\MCregionSnoTVrisingqualitydownBias}{-0.043} | |
| 2153 | +\newcommand{\MCregionSnoTVrisingqualitydownCov}{97} | |
| 2154 | +\newcommand{\MCregionSnoTVrisingqualitydownLag}{+0.00} | |
| 2155 | +\newcommand{\MCregionSnoTVrisingqualitydownRev}{0.59} | |
| 2156 | +\newcommand{\MCregionSnoTVrisingqualitydownRmse}{1.40} | |
| 2157 | +\newcommand{\MCregionSnoTVrisingqualitydownShare}{100} | |
| 2158 | +\newcommand{\MCregionSnoTVsparseBias}{-0.001} | |
| 2159 | +\newcommand{\MCregionSnoTVsparseCov}{99} | |
| 2160 | +\newcommand{\MCregionSnoTVsparseLag}{+0.01} | |
| 2161 | +\newcommand{\MCregionSnoTVsparseRev}{0.81} | |
| 2162 | +\newcommand{\MCregionSnoTVsparseRmse}{2.23} | |
| 2163 | +\newcommand{\MCregionSnoTVsparseShare}{100} | |
| 2164 | +\newcommand{\MCregionSnoTVturningpointBias}{0.000} | |
| 2165 | +\newcommand{\MCregionSnoTVturningpointCov}{99} | |
| 2166 | +\newcommand{\MCregionSnoTVturningpointLag}{+0.00} | |
| 2167 | +\newcommand{\MCregionSnoTVturningpointRev}{0.59} | |
| 2168 | +\newcommand{\MCregionSnoTVturningpointRmse}{1.40} | |
| 2169 | +\newcommand{\MCregionSnoTVturningpointShare}{100} | |
| 2170 | +\newcommand{\MCregionSnorobustbaselineBias}{0.001} | |
| 2171 | +\newcommand{\MCregionSnorobustbaselineCov}{99} | |
| 2172 | +\newcommand{\MCregionSnorobustbaselineLag}{+0.01} | |
| 2173 | +\newcommand{\MCregionSnorobustbaselineRev}{0.60} | |
| 2174 | +\newcommand{\MCregionSnorobustbaselineRmse}{1.70} | |
| 2175 | +\newcommand{\MCregionSnorobustbaselineShare}{100} | |
| 2176 | +\newcommand{\MCregionSnorobustdriftinghedonicsBias}{0.001} | |
| 2177 | +\newcommand{\MCregionSnorobustdriftinghedonicsCov}{99} | |
| 2178 | +\newcommand{\MCregionSnorobustdriftinghedonicsLag}{+0.01} | |
| 2179 | +\newcommand{\MCregionSnorobustdriftinghedonicsRev}{0.61} | |
| 2180 | +\newcommand{\MCregionSnorobustdriftinghedonicsRmse}{1.74} | |
| 2181 | +\newcommand{\MCregionSnorobustdriftinghedonicsShare}{100} | |
| 2182 | +\newcommand{\MCregionSnorobustflatqualityupBias}{0.050} | |
| 2183 | +\newcommand{\MCregionSnorobustflatqualityupCov}{98} | |
| 2184 | +\newcommand{\MCregionSnorobustflatqualityupLag}{-0.02} | |
| 2185 | +\newcommand{\MCregionSnorobustflatqualityupRev}{0.60} | |
| 2186 | +\newcommand{\MCregionSnorobustflatqualityupRmse}{1.70} | |
| 2187 | +\newcommand{\MCregionSnorobustflatqualityupShare}{100} | |
| 2188 | +\newcommand{\MCregionSnorobustheavytailsBias}{-0.003} | |
| 2189 | +\newcommand{\MCregionSnorobustheavytailsCov}{98} | |
| 2190 | +\newcommand{\MCregionSnorobustheavytailsLag}{-0.02} | |
| 2191 | +\newcommand{\MCregionSnorobustheavytailsRev}{0.96} | |
| 2192 | +\newcommand{\MCregionSnorobustheavytailsRmse}{2.72} | |
| 2193 | +\newcommand{\MCregionSnorobustheavytailsShare}{100} | |
| 2194 | +\newcommand{\MCregionSnorobustidiosyncratictownsBias}{-0.003} | |
| 2195 | +\newcommand{\MCregionSnorobustidiosyncratictownsCov}{99} | |
| 2196 | +\newcommand{\MCregionSnorobustidiosyncratictownsLag}{-0.00} | |
| 2197 | +\newcommand{\MCregionSnorobustidiosyncratictownsRev}{0.67} | |
| 2198 | +\newcommand{\MCregionSnorobustidiosyncratictownsRmse}{1.84} | |
| 2199 | +\newcommand{\MCregionSnorobustidiosyncratictownsShare}{100} | |
| 2200 | +\newcommand{\MCregionSnorobustnolocaldynamicsBias}{-0.001} | |
| 2201 | +\newcommand{\MCregionSnorobustnolocaldynamicsCov}{99} | |
| 2202 | +\newcommand{\MCregionSnorobustnolocaldynamicsLag}{+0.00} | |
| 2203 | +\newcommand{\MCregionSnorobustnolocaldynamicsRev}{0.33} | |
| 2204 | +\newcommand{\MCregionSnorobustnolocaldynamicsRmse}{1.58} | |
| 2205 | +\newcommand{\MCregionSnorobustnolocaldynamicsShare}{100} | |
| 2206 | +\newcommand{\MCregionSnorobustrisingqualitydownBias}{-0.045} | |
| 2207 | +\newcommand{\MCregionSnorobustrisingqualitydownCov}{98} | |
| 2208 | +\newcommand{\MCregionSnorobustrisingqualitydownLag}{+0.01} | |
| 2209 | +\newcommand{\MCregionSnorobustrisingqualitydownRev}{0.60} | |
| 2210 | +\newcommand{\MCregionSnorobustrisingqualitydownRmse}{1.70} | |
| 2211 | +\newcommand{\MCregionSnorobustrisingqualitydownShare}{100} | |
| 2212 | +\newcommand{\MCregionSnorobustsparseBias}{0.005} | |
| 2213 | +\newcommand{\MCregionSnorobustsparseCov}{99} | |
| 2214 | +\newcommand{\MCregionSnorobustsparseLag}{+0.02} | |
| 2215 | +\newcommand{\MCregionSnorobustsparseRev}{0.81} | |
| 2216 | +\newcommand{\MCregionSnorobustsparseRmse}{2.70} | |
| 2217 | +\newcommand{\MCregionSnorobustsparseShare}{100} | |
| 2218 | +\newcommand{\MCregionSnorobustturningpointBias}{0.001} | |
| 2219 | +\newcommand{\MCregionSnorobustturningpointCov}{99} | |
| 2220 | +\newcommand{\MCregionSnorobustturningpointLag}{+0.01} | |
| 2221 | +\newcommand{\MCregionSnorobustturningpointRev}{0.60} | |
| 2222 | +\newcommand{\MCregionSnorobustturningpointRmse}{1.70} | |
| 2223 | +\newcommand{\MCregionSnorobustturningpointShare}{100} | |
| 2224 | +\newcommand{\MCregionSsharpbaselineBias}{0.002} | |
| 2225 | +\newcommand{\MCregionSsharpbaselineCov}{99} | |
| 2226 | +\newcommand{\MCregionSsharpbaselineLag}{+0.00} | |
| 2227 | +\newcommand{\MCregionSsharpbaselineRev}{0.59} | |
| 2228 | +\newcommand{\MCregionSsharpbaselineRmse}{1.40} | |
| 2229 | +\newcommand{\MCregionSsharpbaselineShare}{100} | |
| 2230 | +\newcommand{\MCregionSsharpdriftinghedonicsBias}{0.002} | |
| 2231 | +\newcommand{\MCregionSsharpdriftinghedonicsCov}{99} | |
| 2232 | +\newcommand{\MCregionSsharpdriftinghedonicsLag}{+0.00} | |
| 2233 | +\newcommand{\MCregionSsharpdriftinghedonicsRev}{0.59} | |
| 2234 | +\newcommand{\MCregionSsharpdriftinghedonicsRmse}{1.44} | |
| 2235 | +\newcommand{\MCregionSsharpdriftinghedonicsShare}{100} | |
| 2236 | +\newcommand{\MCregionSsharpfilteredbaselineBias}{0.003} | |
| 2237 | +\newcommand{\MCregionSsharpfilteredbaselineCov}{98} | |
| 2238 | +\newcommand{\MCregionSsharpfilteredbaselineLag}{+0.01} | |
| 2239 | +\newcommand{\MCregionSsharpfilteredbaselineRev}{n/a} | |
| 2240 | +\newcommand{\MCregionSsharpfilteredbaselineRmse}{1.58} | |
| 2241 | +\newcommand{\MCregionSsharpfilteredbaselineShare}{100} | |
| 2242 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsBias}{0.003} | |
| 2243 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsCov}{98} | |
| 2244 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsLag}{+0.01} | |
| 2245 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsRev}{n/a} | |
| 2246 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsRmse}{1.62} | |
| 2247 | +\newcommand{\MCregionSsharpfiltereddriftinghedonicsShare}{100} | |
| 2248 | +\newcommand{\MCregionSsharpfilteredflatqualityupBias}{0.052} | |
| 2249 | +\newcommand{\MCregionSsharpfilteredflatqualityupCov}{96} | |
| 2250 | +\newcommand{\MCregionSsharpfilteredflatqualityupLag}{+0.11} | |
| 2251 | +\newcommand{\MCregionSsharpfilteredflatqualityupRev}{n/a} | |
| 2252 | +\newcommand{\MCregionSsharpfilteredflatqualityupRmse}{1.58} | |
| 2253 | +\newcommand{\MCregionSsharpfilteredflatqualityupShare}{100} | |
| 2254 | +\newcommand{\MCregionSsharpfilteredheavytailsBias}{-0.006} | |
| 2255 | +\newcommand{\MCregionSsharpfilteredheavytailsCov}{91} | |
| 2256 | +\newcommand{\MCregionSsharpfilteredheavytailsLag}{+0.01} | |
| 2257 | +\newcommand{\MCregionSsharpfilteredheavytailsRev}{n/a} | |
| 2258 | +\newcommand{\MCregionSsharpfilteredheavytailsRmse}{2.94} | |
| 2259 | +\newcommand{\MCregionSsharpfilteredheavytailsShare}{100} | |
| 2260 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsBias}{-0.000} | |
| 2261 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsCov}{98} | |
| 2262 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsLag}{+0.02} | |
| 2263 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsRev}{n/a} | |
| 2264 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsRmse}{1.76} | |
| 2265 | +\newcommand{\MCregionSsharpfilteredidiosyncratictownsShare}{100} | |
| 2266 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsBias}{0.002} | |
| 2267 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsCov}{99} | |
| 2268 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsLag}{+0.00} | |
| 2269 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsRev}{n/a} | |
| 2270 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsRmse}{1.30} | |
| 2271 | +\newcommand{\MCregionSsharpfilterednolocaldynamicsShare}{100} | |
| 2272 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownBias}{-0.043} | |
| 2273 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownCov}{97} | |
| 2274 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownLag}{+0.01} | |
| 2275 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownRev}{n/a} | |
| 2276 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownRmse}{1.58} | |
| 2277 | +\newcommand{\MCregionSsharpfilteredrisingqualitydownShare}{100} | |
| 2278 | +\newcommand{\MCregionSsharpfilteredsparseBias}{0.004} | |
| 2279 | +\newcommand{\MCregionSsharpfilteredsparseCov}{99} | |
| 2280 | +\newcommand{\MCregionSsharpfilteredsparseLag}{+0.06} | |
| 2281 | +\newcommand{\MCregionSsharpfilteredsparseRev}{n/a} | |
| 2282 | +\newcommand{\MCregionSsharpfilteredsparseRmse}{2.44} | |
| 2283 | +\newcommand{\MCregionSsharpfilteredsparseShare}{100} | |
| 2284 | +\newcommand{\MCregionSsharpfilteredturningpointBias}{0.003} | |
| 2285 | +\newcommand{\MCregionSsharpfilteredturningpointCov}{98} | |
| 2286 | +\newcommand{\MCregionSsharpfilteredturningpointLag}{+0.01} | |
| 2287 | +\newcommand{\MCregionSsharpfilteredturningpointRev}{n/a} | |
| 2288 | +\newcommand{\MCregionSsharpfilteredturningpointRmse}{1.58} | |
| 2289 | +\newcommand{\MCregionSsharpfilteredturningpointShare}{100} | |
| 2290 | +\newcommand{\MCregionSsharpflatqualityupBias}{0.051} | |
| 2291 | +\newcommand{\MCregionSsharpflatqualityupCov}{96} | |
| 2292 | +\newcommand{\MCregionSsharpflatqualityupLag}{+0.03} | |
| 2293 | +\newcommand{\MCregionSsharpflatqualityupRev}{0.59} | |
| 2294 | +\newcommand{\MCregionSsharpflatqualityupRmse}{1.41} | |
| 2295 | +\newcommand{\MCregionSsharpflatqualityupShare}{100} | |
| 2296 | +\newcommand{\MCregionSsharpheavytailsBias}{-0.005} | |
| 2297 | +\newcommand{\MCregionSsharpheavytailsCov}{94} | |
| 2298 | +\newcommand{\MCregionSsharpheavytailsLag}{-0.03} | |
| 2299 | +\newcommand{\MCregionSsharpheavytailsRev}{0.90} | |
| 2300 | +\newcommand{\MCregionSsharpheavytailsRmse}{2.32} | |
| 2301 | +\newcommand{\MCregionSsharpheavytailsShare}{100} | |
| 2302 | +\newcommand{\MCregionSsharpidiosyncratictownsBias}{-0.001} | |
| 2303 | +\newcommand{\MCregionSsharpidiosyncratictownsCov}{98} | |
| 2304 | +\newcommand{\MCregionSsharpidiosyncratictownsLag}{+0.00} | |
| 2305 | +\newcommand{\MCregionSsharpidiosyncratictownsRev}{0.65} | |
| 2306 | +\newcommand{\MCregionSsharpidiosyncratictownsRmse}{1.58} | |
| 2307 | +\newcommand{\MCregionSsharpidiosyncratictownsShare}{100} | |
| 2308 | +\newcommand{\MCregionSsharpnolocaldynamicsBias}{0.000} | |
| 2309 | +\newcommand{\MCregionSsharpnolocaldynamicsCov}{99} | |
| 2310 | +\newcommand{\MCregionSsharpnolocaldynamicsLag}{+0.00} | |
| 2311 | +\newcommand{\MCregionSsharpnolocaldynamicsRev}{0.33} | |
| 2312 | +\newcommand{\MCregionSsharpnolocaldynamicsRmse}{1.23} | |
| 2313 | +\newcommand{\MCregionSsharpnolocaldynamicsShare}{100} | |
| 2314 | +\newcommand{\MCregionSsharprisingqualitydownBias}{-0.045} | |
| 2315 | +\newcommand{\MCregionSsharprisingqualitydownCov}{97} | |
| 2316 | +\newcommand{\MCregionSsharprisingqualitydownLag}{+0.00} | |
| 2317 | +\newcommand{\MCregionSsharprisingqualitydownRev}{0.59} | |
| 2318 | +\newcommand{\MCregionSsharprisingqualitydownRmse}{1.40} | |
| 2319 | +\newcommand{\MCregionSsharprisingqualitydownShare}{100} | |
| 2320 | +\newcommand{\MCregionSsharpsparseBias}{0.000} | |
| 2321 | +\newcommand{\MCregionSsharpsparseCov}{99} | |
| 2322 | +\newcommand{\MCregionSsharpsparseLag}{+0.01} | |
| 2323 | +\newcommand{\MCregionSsharpsparseRev}{0.81} | |
| 2324 | +\newcommand{\MCregionSsharpsparseRmse}{2.24} | |
| 2325 | +\newcommand{\MCregionSsharpsparseShare}{100} | |
| 2326 | +\newcommand{\MCregionSsharpturningpointBias}{0.002} | |
| 2327 | +\newcommand{\MCregionSsharpturningpointCov}{99} | |
| 2328 | +\newcommand{\MCregionSsharpturningpointLag}{+0.00} | |
| 2329 | +\newcommand{\MCregionSsharpturningpointRev}{0.59} | |
| 2330 | +\newcommand{\MCregionSsharpturningpointRmse}{1.40} | |
| 2331 | +\newcommand{\MCregionSsharpturningpointShare}{100} | |
| 217 | 2332 | \newcommand{\MedianGainMuncondo}{0.25} |
| 218 | 2333 | \newcommand{\MedianGainMunhouse}{0.33} |
| 219 | 2334 | \newcommand{\MedianGainMunplex}{0.47} |
@@ -439,24 +2554,24 @@ | ||
| 439 | 2554 | \newcommand{\RegSdSsharpfilteredplex}{3.61} |
| 440 | 2555 | \newcommand{\RegSdSsharphouse}{3.75} |
| 441 | 2556 | \newcommand{\RegSdSsharpplex}{2.56} |
| 442 | −\newcommand{\RevFirstTwelvecondomunicipality}{3.59} | |
| 2557 | +\newcommand{\RevFirstTwelvecondomunicipality}{2.40} | |
| 443 | 2558 | \newcommand{\RevFirstTwelvecondoprovince}{0.25} |
| 444 | −\newcommand{\RevFirstTwelvecondoregion}{1.93} | |
| 445 | −\newcommand{\RevFirstTwelvehousemunicipality}{2.42} | |
| 2559 | +\newcommand{\RevFirstTwelvecondoregion}{1.23} | |
| 2560 | +\newcommand{\RevFirstTwelvehousemunicipality}{2.09} | |
| 446 | 2561 | \newcommand{\RevFirstTwelvehouseprovince}{0.13} |
| 447 | −\newcommand{\RevFirstTwelvehouseregion}{2.24} | |
| 448 | −\newcommand{\RevFirstTwelveplexmunicipality}{5.26} | |
| 2562 | +\newcommand{\RevFirstTwelvehouseregion}{2.16} | |
| 2563 | +\newcommand{\RevFirstTwelveplexmunicipality}{3.95} | |
| 449 | 2564 | \newcommand{\RevFirstTwelveplexprovince}{0.35} |
| 450 | −\newcommand{\RevFirstTwelveplexregion}{2.05} | |
| 451 | −\newcommand{\RevFirstcondomunicipality}{1.82} | |
| 2565 | +\newcommand{\RevFirstTwelveplexregion}{1.55} | |
| 2566 | +\newcommand{\RevFirstcondomunicipality}{0.86} | |
| 452 | 2567 | \newcommand{\RevFirstcondoprovince}{0.16} |
| 453 | −\newcommand{\RevFirstcondoregion}{0.98} | |
| 454 | −\newcommand{\RevFirsthousemunicipality}{2.31} | |
| 2568 | +\newcommand{\RevFirstcondoregion}{0.48} | |
| 2569 | +\newcommand{\RevFirsthousemunicipality}{1.72} | |
| 455 | 2570 | \newcommand{\RevFirsthouseprovince}{0.16} |
| 456 | −\newcommand{\RevFirsthouseregion}{1.93} | |
| 457 | −\newcommand{\RevFirstplexmunicipality}{3.03} | |
| 2571 | +\newcommand{\RevFirsthouseregion}{1.62} | |
| 2572 | +\newcommand{\RevFirstplexmunicipality}{1.58} | |
| 458 | 2573 | \newcommand{\RevFirstplexprovince}{0.26} |
| 459 | −\newcommand{\RevFirstplexregion}{1.37} | |
| 2574 | +\newcommand{\RevFirstplexregion}{0.76} | |
| 460 | 2575 | \newcommand{\RollSpatialAgree}{99.96} |
| 461 | 2576 | \newcommand{\RtwoWithincondo}{0.63} |
| 462 | 2577 | \newcommand{\RtwoWithinhouse}{0.53} |
added
paper/tables/tab_mc_bias.tex
+18 −0
@@ -0,0 +1,18 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\scriptsize | |
| 3 | +\caption{Monte Carlo: mean bias of monthly log changes (×100) at the municipality level}\label{tab:mc_bias} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrrrrrr} | |
| 6 | +\toprule | |
| 7 | +Method & baseline & turning point & flat quality up & rising quality down & drifting hedonics & heavy tails & sparse & no local dynamics & idiosyncratic towns \\ | |
| 8 | +\midrule | |
| 9 | +Median & -0.043 & -0.043 & +0.211 & -0.282 & -0.040 & +0.093 & +0.049 & -0.043 & -0.002 \\ | |
| 10 | +Pooled TD (Huber) & -0.018 & -0.018 & +0.029 & -0.064 & -0.019 & -0.016 & -0.012 & -0.018 & -0.005 \\ | |
| 11 | +QHPI v2.1 arch. & -0.001 & -0.000 & +0.046 & -0.045 & +0.001 & -0.005 & -0.005 & -0.001 & -0.010 \\ | |
| 12 | +SHARP-HPI (smoothed) & +0.003 & +0.003 & +0.052 & -0.043 & +0.004 & -0.005 & +0.004 & +0.002 & -0.005 \\ | |
| 13 | +SHARP-HPI (filtered) & +0.001 & +0.001 & +0.050 & -0.045 & +0.002 & -0.012 & +0.008 & +0.001 & +0.005 \\ | |
| 14 | +\bottomrule | |
| 15 | +\end{tabular} | |
| 16 | + | |
| 17 | +\end{threeparttable} | |
| 18 | +\end{table} | |
added
paper/tables/tab_mc_coverage.tex
+20 −0
@@ -0,0 +1,20 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\scriptsize | |
| 3 | +\caption{Monte Carlo: empirical coverage (\%) of nominal 95\% intervals for municipal index levels}\label{tab:mc_coverage} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrrrrrr} | |
| 6 | +\toprule | |
| 7 | +Method & baseline & turning point & flat quality up & rising quality down & drifting hedonics & heavy tails & sparse & no local dynamics & idiosyncratic towns \\ | |
| 8 | +\midrule | |
| 9 | +Pooled TD (Huber) & 97 & 97 & 97 & 97 & 97 & 97 & 96 & 97 & 97 \\ | |
| 10 | +QHPI v2.1 arch. & 89 & 89 & 87 & 87 & 89 & 83 & 89 & 90 & 89 \\ | |
| 11 | +SHARP-HPI (smoothed) & 98 & 98 & 97 & 98 & 98 & 92 & 99 & 99 & 96 \\ | |
| 12 | +SHARP-HPI (filtered) & 97 & 97 & 96 & 96 & 97 & 89 & 98 & 98 & 95 \\ | |
| 13 | +SHARP: no EB pooling of $\tau^2$ & 97 & 97 & 96 & 96 & 98 & 91 & 98 & 99 & 93 \\ | |
| 14 | +SHARP: local linear trend & 97 & 97 & 96 & 96 & 97 & 90 & 97 & 98 & 93 \\ | |
| 15 | +SHARP: Gaussian & 98 & 98 & 97 & 97 & 98 & 92 & 98 & 98 & 97 \\ | |
| 16 | +\bottomrule | |
| 17 | +\end{tabular} | |
| 18 | + | |
| 19 | +\end{threeparttable} | |
| 20 | +\end{table} | |
added
paper/tables/tab_mc_lag.tex
+22 −0
@@ -0,0 +1,22 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\small | |
| 3 | +\caption{Monte Carlo: timing diagnostics at the municipality level}\label{tab:mc_lag} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrr} | |
| 6 | +\toprule | |
| 7 | +Scenario & Reps & SHARP lag (months) & SHARP revision RMSE & QHPI lag & sec./rep \\ | |
| 8 | +\midrule | |
| 9 | +baseline & 80 & +0.01 & 2.36 & +0.01 & — \\ | |
| 10 | +turning point & 80 & +0.00 & 2.36 & +0.02 & — \\ | |
| 11 | +flat quality up & 80 & +0.01 & 2.37 & +0.03 & — \\ | |
| 12 | +rising quality down & 80 & +0.01 & 2.37 & +0.02 & — \\ | |
| 13 | +drifting hedonics & 80 & +0.01 & 2.39 & +0.01 & — \\ | |
| 14 | +heavy tails & 80 & -0.01 & 4.80 & -0.00 & — \\ | |
| 15 | +sparse & 80 & +0.02 & 3.16 & +0.01 & — \\ | |
| 16 | +no local dynamics & 80 & +0.00 & 2.20 & -0.00 & — \\ | |
| 17 | +idiosyncratic towns & 80 & +0.02 & 3.10 & -0.00 & — \\ | |
| 18 | +\bottomrule | |
| 19 | +\end{tabular} | |
| 20 | +\begin{tablenotes}\footnotesize\item Lag = argmax of the cross-correlation between estimated and true 3-month changes (negative = estimate lags). Revision RMSE = RMSE between filtered (real-time) and smoothed monthly changes (×100).\end{tablenotes} | |
| 21 | +\end{threeparttable} | |
| 22 | +\end{table} | |
added
paper/tables/tab_mc_rmse_municipality.tex
+27 −0
@@ -0,0 +1,27 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\scriptsize | |
| 3 | +\caption{Monte Carlo: RMSE of monthly log changes (×100) at the municipality level, by scenario (Monte Carlo standard error in parentheses)}\label{tab:mc_rmse_municipality} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrrrrrr} | |
| 6 | +\toprule | |
| 7 | +Method & baseline & turning point & flat quality up & rising quality down & drifting hedonics & heavy tails & sparse & no local dynamics & idiosyncratic towns \\ | |
| 8 | +\midrule | |
| 9 | +Median & 24.28 (0.16) & 24.28 (0.16) & 24.71 (0.16) & 23.95 (0.16) & 25.89 (0.17) & 27.37 (0.22) & 29.43 (0.13) & 24.28 (0.16) & 24.38 (0.15) \\ | |
| 10 | +Stratified median & 6.01 (0.02) & 6.02 (0.02) & 6.00 (0.02) & 6.00 (0.02) & 6.25 (0.02) & 7.60 (0.03) & 5.57 (0.03) & 5.97 (0.02) & 6.16 (0.02) \\ | |
| 11 | +Pooled TD (Huber) & 12.52 (0.10) & 12.52 (0.10) & 12.55 (0.10) & 12.54 (0.10) & 13.01 (0.11) & 15.15 (0.16) & 15.24 (0.14) & 12.52 (0.10) & 12.57 (0.11) \\ | |
| 12 | +Rolling TD 13 m & 7.48 (0.10) & 7.48 (0.10) & 7.50 (0.10) & 7.48 (0.10) & 7.75 (0.10) & 8.14 (0.13) & 7.84 (0.19) & 7.48 (0.10) & 7.48 (0.10) \\ | |
| 13 | +Repeat sales & 39.26 (0.45) & 39.23 (0.46) & 39.08 (0.44) & 39.80 (0.47) & 39.49 (0.45) & 51.70 (0.99) & 42.97 (0.84) & 39.25 (0.45) & 39.17 (0.42) \\ | |
| 14 | +QHPI v2.1 arch. & 2.91 (0.05) & 2.91 (0.05) & 2.90 (0.05) & 2.91 (0.05) & 2.93 (0.05) & 7.89 (0.07) & 3.57 (0.04) & 2.84 (0.05) & 3.30 (0.04) \\ | |
| 15 | +SHARP-HPI (smoothed) & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.62 (0.02) & 4.62 (0.08) & 2.40 (0.04) & 1.35 (0.02) & 2.52 (0.02) \\ | |
| 16 | +SHARP-HPI (filtered) & 3.02 (0.04) & 3.02 (0.04) & 3.02 (0.04) & 3.02 (0.04) & 3.06 (0.04) & 8.23 (0.10) & 4.17 (0.06) & 2.73 (0.04) & 4.24 (0.03) \\ | |
| 17 | +SHARP: no EB pooling of $\tau^2$ & 1.62 (0.02) & 1.62 (0.02) & 1.62 (0.02) & 1.62 (0.02) & 1.66 (0.02) & 4.51 (0.08) & 2.45 (0.04) & 1.40 (0.02) & 2.55 (0.02) \\ | |
| 18 | +SHARP: fixed quality prices & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.61 (0.02) & 4.62 (0.08) & 2.40 (0.04) & 1.35 (0.02) & 2.52 (0.02) \\ | |
| 19 | +SHARP: local linear trend & 1.61 (0.02) & 1.61 (0.02) & 1.61 (0.02) & 1.61 (0.02) & 1.65 (0.02) & 4.95 (0.08) & 2.45 (0.04) & 1.40 (0.02) & 2.51 (0.02) \\ | |
| 20 | +SHARP: Gaussian & 2.25 (0.04) & 2.25 (0.04) & 2.24 (0.04) & 2.23 (0.04) & 2.29 (0.04) & 6.77 (0.09) & 3.21 (0.05) & 2.13 (0.04) & 2.84 (0.03) \\ | |
| 21 | +SHARP: municipality-level location FE & 2.05 (0.04) & 2.05 (0.04) & 2.05 (0.04) & 2.05 (0.04) & 2.08 (0.04) & 2.84 (0.06) & 3.30 (0.08) & 1.88 (0.04) & 2.81 (0.03) \\ | |
| 22 | +SHARP: stock-weighted bottom-up & — & — & — & — & — & — & — & — & — \\ | |
| 23 | +\bottomrule | |
| 24 | +\end{tabular} | |
| 25 | +\begin{tablenotes}\footnotesize\item Averages over cells and replications; see Table~\ref{tab:mc_design} for the scenarios. Lower is better; the final two scenarios are the deliberate counter-examples.\end{tablenotes} | |
| 26 | +\end{threeparttable} | |
| 27 | +\end{table} | |
added
paper/tables/tab_mc_rmse_province.tex
+27 −0
@@ -0,0 +1,27 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\scriptsize | |
| 3 | +\caption{Monte Carlo: RMSE of monthly log changes (×100) at the province level, by scenario (Monte Carlo standard error in parentheses)}\label{tab:mc_rmse_province} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrrrrrr} | |
| 6 | +\toprule | |
| 7 | +Method & baseline & turning point & flat quality up & rising quality down & drifting hedonics & heavy tails & sparse & no local dynamics & idiosyncratic towns \\ | |
| 8 | +\midrule | |
| 9 | +Median & 3.56 (0.08) & 3.56 (0.08) & 3.62 (0.08) & 3.55 (0.08) & 3.69 (0.08) & 3.59 (0.09) & 6.07 (0.13) & 3.56 (0.08) & 3.62 (0.08) \\ | |
| 10 | +Stratified median & 3.08 (0.07) & 3.08 (0.07) & 3.13 (0.08) & 3.06 (0.07) & 3.14 (0.07) & 3.11 (0.08) & 5.21 (0.13) & 3.07 (0.07) & 3.15 (0.09) \\ | |
| 11 | +Pooled TD (Huber) & 1.24 (0.02) & 1.24 (0.02) & 1.25 (0.02) & 1.24 (0.02) & 1.29 (0.03) & 1.24 (0.02) & 2.13 (0.04) & 1.22 (0.02) & 1.37 (0.03) \\ | |
| 12 | +Rolling TD 13 m & 1.39 (0.03) & 1.39 (0.03) & 1.40 (0.03) & 1.39 (0.03) & 1.44 (0.03) & 1.41 (0.03) & 2.36 (0.05) & 1.39 (0.03) & 1.44 (0.03) \\ | |
| 13 | +Repeat sales & 5.32 (0.10) & 5.32 (0.10) & 5.33 (0.10) & 5.33 (0.10) & 5.37 (0.10) & 7.42 (0.17) & 9.57 (0.21) & 5.28 (0.10) & 5.66 (0.13) \\ | |
| 14 | +QHPI v2.1 arch. & 1.39 (0.03) & 1.39 (0.03) & 1.40 (0.03) & 1.39 (0.03) & 1.44 (0.03) & 1.41 (0.03) & 2.36 (0.05) & 1.39 (0.03) & 1.44 (0.03) \\ | |
| 15 | +SHARP-HPI (smoothed) & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.03) & 1.29 (0.03) & 1.26 (0.02) & 2.14 (0.04) & 1.22 (0.02) & 1.37 (0.03) \\ | |
| 16 | +SHARP-HPI (filtered) & — & — & — & — & — & — & — & — & — \\ | |
| 17 | +SHARP: no EB pooling of $\tau^2$ & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.03) & 1.29 (0.03) & 1.26 (0.02) & 2.14 (0.04) & 1.22 (0.02) & 1.37 (0.03) \\ | |
| 18 | +SHARP: fixed quality prices & 1.25 (0.02) & 1.25 (0.02) & 1.26 (0.02) & 1.25 (0.03) & 1.29 (0.03) & 1.26 (0.02) & 2.13 (0.04) & 1.22 (0.02) & 1.37 (0.03) \\ | |
| 19 | +SHARP: local linear trend & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.02) & 1.25 (0.03) & 1.29 (0.03) & 1.26 (0.02) & 2.14 (0.04) & 1.22 (0.02) & 1.37 (0.03) \\ | |
| 20 | +SHARP: Gaussian & 1.59 (0.03) & 1.59 (0.03) & 1.59 (0.03) & 1.59 (0.03) & 1.63 (0.03) & 2.25 (0.05) & 2.62 (0.05) & 1.57 (0.03) & 1.68 (0.04) \\ | |
| 21 | +SHARP: municipality-level location FE & 1.88 (0.04) & 1.88 (0.04) & 1.88 (0.04) & 1.88 (0.04) & 1.92 (0.05) & 1.96 (0.05) & 3.18 (0.09) & 1.87 (0.04) & 1.96 (0.05) \\ | |
| 22 | +SHARP: stock-weighted bottom-up & 1.26 (0.02) & 1.26 (0.02) & 1.27 (0.02) & 1.26 (0.03) & 1.31 (0.03) & 1.75 (0.04) & 2.15 (0.04) & 1.23 (0.02) & 1.40 (0.03) \\ | |
| 23 | +\bottomrule | |
| 24 | +\end{tabular} | |
| 25 | +\begin{tablenotes}\footnotesize\item Averages over cells and replications; see Table~\ref{tab:mc_design} for the scenarios. Lower is better; the final two scenarios are the deliberate counter-examples.\end{tablenotes} | |
| 26 | +\end{threeparttable} | |
| 27 | +\end{table} | |
added
paper/tables/tab_mc_rmse_region.tex
+27 −0
@@ -0,0 +1,27 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\scriptsize | |
| 3 | +\caption{Monte Carlo: RMSE of monthly log changes (×100) at the region level, by scenario (Monte Carlo standard error in parentheses)}\label{tab:mc_rmse_region} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrrrrrrr} | |
| 6 | +\toprule | |
| 7 | +Method & baseline & turning point & flat quality up & rising quality down & drifting hedonics & heavy tails & sparse & no local dynamics & idiosyncratic towns \\ | |
| 8 | +\midrule | |
| 9 | +Median & 7.24 (0.16) & 7.24 (0.16) & 7.34 (0.16) & 7.19 (0.16) & 7.56 (0.16) & 7.34 (0.17) & 12.37 (0.26) & 7.20 (0.15) & 7.49 (0.14) \\ | |
| 10 | +Stratified median & 6.03 (0.13) & 6.03 (0.13) & 6.12 (0.13) & 6.09 (0.14) & 6.18 (0.14) & 6.35 (0.14) & 9.12 (0.16) & 6.01 (0.13) & 6.47 (0.13) \\ | |
| 11 | +Pooled TD (Huber) & 2.63 (0.04) & 2.63 (0.04) & 2.64 (0.04) & 2.63 (0.04) & 2.73 (0.04) & 2.56 (0.04) & 4.64 (0.07) & 2.61 (0.04) & 2.89 (0.05) \\ | |
| 12 | +Rolling TD 13 m & 2.99 (0.05) & 2.99 (0.05) & 3.00 (0.05) & 2.99 (0.05) & 3.10 (0.05) & 2.99 (0.05) & 5.45 (0.09) & 2.99 (0.05) & 3.11 (0.05) \\ | |
| 13 | +Repeat sales & 12.03 (0.21) & 12.03 (0.21) & 12.05 (0.21) & 12.04 (0.20) & 12.18 (0.21) & 15.80 (0.30) & 22.22 (0.39) & 12.00 (0.21) & 12.59 (0.22) \\ | |
| 14 | +QHPI v2.1 arch. & 1.17 (0.01) & 1.23 (0.01) & 0.84 (0.01) & 1.25 (0.01) & 1.19 (0.01) & 1.21 (0.01) & 2.14 (0.06) & 1.08 (0.01) & 1.27 (0.01) \\ | |
| 15 | +SHARP-HPI (smoothed) & 1.40 (0.02) & 1.40 (0.02) & 1.41 (0.02) & 1.40 (0.02) & 1.44 (0.02) & 2.32 (0.06) & 2.24 (0.04) & 1.23 (0.02) & 1.58 (0.03) \\ | |
| 16 | +SHARP-HPI (filtered) & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.58 (0.02) & 1.62 (0.02) & 2.94 (0.07) & 2.44 (0.04) & 1.30 (0.02) & 1.76 (0.03) \\ | |
| 17 | +SHARP: no EB pooling of $\tau^2$ & 1.39 (0.02) & 1.39 (0.02) & 1.40 (0.02) & 1.39 (0.02) & 1.43 (0.02) & 2.16 (0.06) & 2.24 (0.04) & 1.24 (0.02) & 1.58 (0.03) \\ | |
| 18 | +SHARP: fixed quality prices & 1.40 (0.02) & 1.40 (0.02) & 1.41 (0.02) & 1.40 (0.02) & 1.44 (0.02) & 2.32 (0.06) & 2.23 (0.04) & 1.23 (0.02) & 1.58 (0.03) \\ | |
| 19 | +SHARP: local linear trend & 1.39 (0.02) & 1.39 (0.02) & 1.40 (0.02) & 1.39 (0.02) & 1.43 (0.02) & 2.34 (0.06) & 2.23 (0.04) & 1.23 (0.02) & 1.57 (0.03) \\ | |
| 20 | +SHARP: Gaussian & 1.70 (0.03) & 1.70 (0.03) & 1.70 (0.03) & 1.70 (0.03) & 1.74 (0.03) & 2.72 (0.07) & 2.70 (0.05) & 1.58 (0.03) & 1.84 (0.03) \\ | |
| 21 | +SHARP: municipality-level location FE & 1.97 (0.04) & 1.97 (0.04) & 1.97 (0.04) & 1.97 (0.04) & 2.01 (0.04) & 2.18 (0.06) & 3.24 (0.08) & 1.87 (0.04) & 2.11 (0.04) \\ | |
| 22 | +SHARP: stock-weighted bottom-up & — & — & — & — & — & — & — & — & — \\ | |
| 23 | +\bottomrule | |
| 24 | +\end{tabular} | |
| 25 | +\begin{tablenotes}\footnotesize\item Averages over cells and replications; see Table~\ref{tab:mc_design} for the scenarios. Lower is better; the final two scenarios are the deliberate counter-examples.\end{tablenotes} | |
| 26 | +\end{threeparttable} | |
| 27 | +\end{table} | |
modified
paper/tables/tab_revisions.tex
+6 −6
@@ -7,14 +7,14 @@ | ||
| 7 | 7 | Class / level & Rel. 1 & Rel. 2 & Rel. 3 & Rel. 6 & Rel. 12 & 12-m change, rel. 1 & \% $|$rev$|>1$ pt \\ |
| 8 | 8 | \midrule |
| 9 | 9 | house / province & 0.16 & 0.17 & 0.16 & 0.15 & 0.15 & 0.13 & 0 \\ |
| 10 | −house / region & 1.93 & 1.95 & 1.96 & 1.91 & 1.94 & 2.24 & 53 \\ | |
| 11 | −house / municipality & 2.31 & 2.31 & 2.33 & 2.35 & 2.43 & 2.42 & 65 \\ | |
| 10 | +house / region & 1.62 & 1.56 & 1.57 & 1.51 & 1.59 & 2.16 & 44 \\ | |
| 11 | +house / municipality & 1.72 & 1.61 & 1.59 & 1.55 & 1.62 & 2.09 & 54 \\ | |
| 12 | 12 | condo / province & 0.16 & 0.16 & 0.16 & 0.17 & 0.16 & 0.25 & 0 \\ |
| 13 | −condo / region & 0.98 & 0.98 & 1.00 & 1.04 & 0.98 & 1.93 & 34 \\ | |
| 14 | −condo / municipality & 1.82 & 1.83 & 1.83 & 1.88 & 1.87 & 3.59 & 54 \\ | |
| 13 | +condo / region & 0.48 & 0.43 & 0.43 & 0.43 & 0.39 & 1.23 & 12 \\ | |
| 14 | +condo / municipality & 0.86 & 0.78 & 0.74 & 0.73 & 0.66 & 2.40 & 27 \\ | |
| 15 | 15 | plex / province & 0.26 & 0.25 & 0.26 & 0.27 & 0.24 & 0.35 & 0 \\ |
| 16 | −plex / region & 1.37 & 1.38 & 1.36 & 1.35 & 1.41 & 2.05 & 53 \\ | |
| 17 | −plex / municipality & 3.03 & 3.03 & 3.03 & 3.05 & 3.00 & 5.26 & 73 \\ | |
| 16 | +plex / region & 0.76 & 0.64 & 0.59 & 0.57 & 0.61 & 1.55 & 26 \\ | |
| 17 | +plex / municipality & 1.58 & 1.42 & 1.35 & 1.29 & 1.27 & 3.95 & 48 \\ | |
| 18 | 18 | \bottomrule |
| 19 | 19 | \end{tabular} |
| 20 | 20 | \begin{tablenotes}\footnotesize\item Releases simulated monthly from July 2022 to October 2026 with a 10-day publication delay; municipalities with at least 1,500 sales. Final = smoothed full-sample panel.\end{tablenotes} |
added
paper/tables/tab_revisions_frozen.tex
+22 −0
@@ -0,0 +1,22 @@ | ||
| 1 | +\begin{table}[htbp] | |
| 2 | +\centering\footnotesize | |
| 3 | +\caption{Frozen-first-release (one-sided filtered) policy: mean absolute gap of the monthly log change (×100) between the real-time value and the final smoothed estimate}\label{tab:revisions_frozen} | |
| 4 | +\begin{threeparttable} | |
| 5 | +\begin{tabular}{lrrrr} | |
| 6 | +\toprule | |
| 7 | +Class / level & Rel. 1 vs final & Rel. 12 vs final & 12-m change, rel. 1 & Rel. 1 vs last real-time \\ | |
| 8 | +\midrule | |
| 9 | +house / province & 0.16 & 0.15 & 0.13 & 0.16 \\ | |
| 10 | +house / region & 1.93 & 1.94 & 2.24 & 1.78 \\ | |
| 11 | +house / municipality & 2.31 & 2.43 & 2.42 & 1.61 \\ | |
| 12 | +condo / province & 0.16 & 0.16 & 0.25 & 0.16 \\ | |
| 13 | +condo / region & 0.98 & 0.98 & 1.93 & 0.61 \\ | |
| 14 | +condo / municipality & 1.82 & 1.87 & 3.59 & 1.07 \\ | |
| 15 | +plex / province & 0.26 & 0.24 & 0.35 & 0.26 \\ | |
| 16 | +plex / region & 1.37 & 1.41 & 2.05 & 0.86 \\ | |
| 17 | +plex / municipality & 3.03 & 3.00 & 5.26 & 1.99 \\ | |
| 18 | +\bottomrule | |
| 19 | +\end{tabular} | |
| 20 | +\begin{tablenotes}\footnotesize\item Under this policy a month's value changes only through stage-1 re-estimation; the gap to the final series is the price of never revising (filter versus smoother), and it does not decline with later releases.\end{tablenotes} | |
| 21 | +\end{threeparttable} | |
| 22 | +\end{table} | |
added
results/simulation/summary.csv
+478 −0
@@ -0,0 +1,478 @@ | ||
| 1 | +scenario,method,level,cells,reps,rmse_ret,mae_ret,bias_ret,rmse_lvl,cov95,cov95_ret,corr_ret,lag_xcorr,revision_rmse,share_est,secs,rmse_ret_mcse,bias_ret_mcse,rmse_lvl_mcse,cov95_mcse | |
| 2 | +baseline,S_LLT,mrc,12.0,80,0.014513113819134876,0.01150355982064428,0.000025966266502527645,0.015169438655194356,0.9724479166666666,0.9988524011299434,0.618674904112949,0.0010416666666666667,0.01831700855640733,1.0,,0.00021400126275666342,0.00003731708402763646,0.00017514826150822436,0.0012386783513900835 | |
| 3 | +baseline,S_sharp,mrc,12.0,80,0.014605799321126328,0.011589915130972281,0.00002514115531258226,0.014891299038037518,0.9819444444444445,0.9994526836158192,0.6201845357073686,0.0010416666666666667,0.01107826563452112,1.0,,0.00021551793423144527,0.00003654772626977688,0.00017464108954526553,0.0009492205700345215 | |
| 4 | +baseline,S_noTV,mrc,12.0,80,0.01460770549535298,0.011589751397715065,0.00001529973880103845,0.01487190371030066,0.9816145833333334,0.9994350282485875,0.6200560091378575,0.0,0.011076104614760997,1.0,,0.00021528214593427358,0.000036000141652039424,0.00017340480600232277,0.0009412264942077335 | |
| 5 | +baseline,S_noEB,mrc,12.0,80,0.014622675089077237,0.011600389920437828,0.000026238616506327612,0.015132009040579533,0.9740104166666667,0.9990112994350282,0.6179348320824889,-0.003125,0.01050039247673299,1.0,,0.00021454532986910194,0.00003700417936820591,0.00017780149698259437,0.0012119627153377645 | |
| 6 | +baseline,S_norobust,mrc,12.0,80,0.017688845220717005,0.01395465957460105,0.000018348041757797054,0.016472618946644462,0.9880555555555555,0.9990289548022598,0.5459867396788944,0.0010416666666666669,0.01223841562438747,1.0,,0.0002900443617238205,0.000042148175086605516,0.00021072530579056416,0.0008424402756666626 | |
| 7 | +baseline,K_qhpi,mrc,12.0,80,0.018484193463712967,0.014678138652682077,-0.000027592967494485795,0.019288883189992626,0.8742534722222223,0.9603107344632769,0.5198162493605771,6.938893903907229e-19,,1.0,,0.00034468205949764256,0.00003693437708746401,0.00031273679466574226,0.004011097772484992 | |
| 8 | +baseline,S_sharp_filtered,mrc,12.0,80,0.019370547538120054,0.015068761367110838,0.00003195111232912047,0.02036973179887484,0.9702604166666667,0.9986052259887005,0.5307671332188557,0.04375,,1.0,,0.00026129360240056835,0.00004797756510045512,0.0002711807903660172,0.0010957016889253381 | |
| 9 | +baseline,S_coarseloc,mrc,12.0,80,0.020147041504940517,0.015768210813789006,0.00011070907373949182,0.01853866986285089,0.98671875,0.9979166666666666,0.4944002586345162,0.011458333333333333,0.013076881545571203,1.0,,0.0004164047124550452,0.00005310666572647908,0.00028774616903301395,0.001022545956995307 | |
| 10 | +baseline,C_pooled_td,mrc,12.0,80,0.05400356494437021,0.04139706209514106,0.00004025395444462962,0.03831944168977412,0.9783443332379589,0.9940226569487628,0.284268318324086,0.02291666666666667,,0.9993055555555556,,0.0008578893973480193,0.00005338217902961887,0.000605581311430648,0.00030212740871728226 | |
| 11 | +baseline,D_rtd13,mrc,11.2875,80,0.05917304813052898,0.04545988966096976,0.000028137471990010686,0.04270878321845929,,,0.26191428558881574,-0.042411616161616164,,0.9998892045454545,,0.0008548463490173445,0.00005840333977526823,0.0006060144550823392, | |
| 12 | +baseline,B_stratified,mrc,12.0,80,0.08467449135481553,0.06667347693881638,0.0001121338506202815,0.06522185139279843,,,0.16652356930148884,0.03958333333333334,,0.996857638888889,,0.0008610878225240404,0.00007339058079740892,0.0007077406753411401, | |
| 13 | +baseline,A_median,mrc,12.0,80,0.1299928045690438,0.1010042340969924,-0.0000105838545564354,0.0922352263111405,,,0.12786388555395956,0.053125,,0.9993055555555556,,0.0020160391331075196,0.00012100040230492695,0.0014327679664532889, | |
| 14 | +baseline,H_repeat_sales,mrc,12.0,80,0.25160886557020123,0.19107342560582533,-0.0003847649209769692,0.17906817990805893,,,0.06225837063868549,-0.057499999999999996,,0.9325520833333334,,0.003493542497542152,0.0004349139351770073,0.002545423047771918, | |
| 15 | +baseline,S_sharp,municipality,60.0,80,0.015767583366925075,0.012504440634791072,0.000027787239995214433,0.022267671324836764,0.9838055555555556,0.9998764124293785,0.5933814590456945,0.0060416666666666665,0.023648978529205895,1.0,,0.00021809208535830373,0.00004014949855295353,0.0003339941299079501,0.0006090298275396282 | |
| 16 | +baseline,S_noTV,municipality,60.0,80,0.01578150824266774,0.012513211563928145,0.00001816141993945365,0.022279741034473128,0.9837430555555555,0.9998658192090396,0.5930988752296005,0.004583333333333333,0.023666678675871432,1.0,,0.0002179602904365813,0.00003972076149357516,0.00033333796505387444,0.0006151845881302979 | |
| 17 | +baseline,S_LLT,municipality,60.0,80,0.016092097792137893,0.012740830061633198,0.0000336933082123361,0.02825596396421477,0.9674305555555556,0.9997669491525423,0.5877278135425902,0.009375,0.04570115383649295,1.0,,0.0002159579024704687,0.00004597944269768983,0.0004340550304747036,0.0008997433586438193 | |
| 18 | +baseline,S_noEB,municipality,60.0,80,0.016182591187484083,0.012821917314294972,0.000027247448416850393,0.023882126490996212,0.9749236111111111,0.9997139830508475,0.585877985772756,0.007291666666666666,0.023746687160629297,1.0,,0.00021375177862183536,0.000041729471396639964,0.0003430354488044203,0.000795725693071329 | |
| 19 | +baseline,S_coarseloc,municipality,60.0,80,0.02045659134718862,0.016032373621684633,0.00010692589497120942,0.020958627534755685,0.9940520833333334,0.9995727401129944,0.48128686756764927,0.014583333333333334,0.026703889317457662,1.0,,0.0004081843009769978,0.00005076350047581947,0.000248521101243998,0.0003246605927103843 | |
| 20 | +baseline,S_norobust,municipality,60.0,80,0.022451113324421526,0.017323131843697322,0.00003853791639300957,0.030283038031646548,0.9795138888888889,0.9982803672316385,0.4956707092774163,0.005833333333333334,0.030366838522902867,1.0,,0.00037571698571372967,0.00004995930971391265,0.0005612274921039576,0.0006943477429155293 | |
| 21 | +baseline,K_qhpi,municipality,60.0,80,0.02912435158080009,0.022103731307768125,-8.590343876137581e-6,0.04250209129038594,0.8857777777777777,0.9753672316384181,0.45860793964954283,0.014791666666666667,,1.0,,0.0004618386848896121,0.00004747280612578862,0.0006233292187931513,0.0021228403621829433 | |
| 22 | +baseline,S_sharp_filtered,municipality,60.0,80,0.030152879628772167,0.020591640129909577,0.000010466624616440565,0.03973358339206013,0.9686319444444444,0.9989477401129943,0.4012894676884747,0.11458333333333333,,1.0,,0.0003724900294663904,0.00006185021210181865,0.0005354064024768421,0.000716999654839423 | |
| 23 | +baseline,B_stratified,municipality,49.1875,80,0.06006046765694238,0.04508118376622745,-0.00009665655373670938,0.0653353724602559,,,0.10513064463889607,0.4210204451833238,,0.9679143623352203,,0.00018699782703210897,0.000037700498346550314,0.0002297481446944421, | |
| 24 | +baseline,D_rtd13,municipality,11.8375,80,0.07476283438807321,0.05671572155281137,0.000018144954175337207,0.05398721074972035,,,0.21447958425782915,0.000684394241379533,,0.9994519916188114,,0.0009692166139521838,0.00005928587886621716,0.0006597109469135316, | |
| 25 | +baseline,C_pooled_td,municipality,39.2625,80,0.1252188152832185,0.0924427380603682,-0.00017885873604604475,0.08980898457869561,0.9677307837932808,0.9830416220664222,0.14909010517492638,0.009311205703654865,,0.9565275781612005,,0.0010064267605421888,0.00011918031284983386,0.0006855176040788598,0.0003902054629756159 | |
| 26 | +baseline,A_median,municipality,60.0,80,0.24275115114555348,0.19000019532092757,-0.0004303374215955661,0.17293378571644794,,,0.07338842769004708,-0.0021587023750580365,,0.815420138888889,,0.0016306233554351914,0.0006524609448272029,0.001163957125309774, | |
| 27 | +baseline,H_repeat_sales,municipality,60.0,80,0.3926462753971506,0.3052504790177827,0.00010963847857366543,0.27651605989195327,,,0.033540304078117776,-0.09769063626837013,,0.3024444444444444,,0.004541735387945538,0.0008704469525744211,0.002924998784007821, | |
| 28 | +baseline,C_pooled_td,province,1.0,80,0.012424617420623316,0.009745330868391805,6.429501457506367e-7,0.008796330745568246,0.9806250000000001,0.9970338983050848,0.7063362464009889,0.0,,1.0,0.6011751800775528,0.0002475761799787347,0.00003173756523305051,0.0001651555780300901,0.0008089116640083608 | |
| 29 | +baseline,S_sharp,province,1.0,80,0.012474238074506542,0.00978775662110408,0.000010686954839893348,0.008848463830013049,0.9806250000000001,0.9972457627118644,0.7048748240104885,0.0,,1.0,2.6359437197446822,0.0002488263404219064,0.0000320527959359459,0.0001672431165427482,0.0008089116640083607 | |
| 30 | +baseline,S_noEB,province,1.0,80,0.012474238074506542,0.00978775662110408,0.00001068695483989335,0.008848463830013049,0.9806250000000001,0.9972457627118644,0.7048748240104885,0.0,,1.0,2.3591555297374724,0.0002488263404219064,0.00003205279593594589,0.00016724311654274805,0.0008089116640083601 | |
| 31 | +baseline,S_LLT,province,1.0,80,0.012474238074506542,0.00978775662110408,0.000010686954839893348,0.008848463830013049,0.9806250000000001,0.9972457627118644,0.7048748240104885,0.0,,1.0,15.270315244793892,0.0002488263404219064,0.00003205279593594589,0.00016724311654274813,0.0008089116640083582 | |
| 32 | +baseline,S_noTV,province,1.0,80,0.01247788424931105,0.009785574434437826,1.0556835306875045e-7,0.00883238501072054,0.9808333333333333,0.9972457627118644,0.7047020681236955,0.0,,1.0,2.668670988082886,0.0002484544431242729,0.0000318361181982849,0.00016595977805587193,0.0007900132634326047 | |
| 33 | +baseline,S_sharp_bottomup,province,1.0,80,0.012641800624443436,0.009931788244530455,0.000012240064984916655,0.009218023589517173,,,0.7003148237342259,0.0,,1.0,,0.0002483517836196716,0.000034886587165275566,0.00017203379376179518, | |
| 34 | +baseline,D_rtd13,province,1.0,80,0.013914067442239808,0.010857995506017319,-0.00002642076226693264,0.010055530504417466,,,0.6694417947971305,0.0,,1.0,6.439088591933251,0.0002788929163027603,0.00003838980035991135,0.00019233980642260423, | |
| 35 | +baseline,K_qhpi,province,1.0,80,0.013914067442239808,0.010857995506017319,-0.00002642076226693264,0.010055530504417466,,,0.6694417947971305,0.0,,1.0,6.568549185991287,0.0002788929163027602,0.00003838980035991135,0.00019233980642260423, | |
| 36 | +baseline,S_norobust,province,1.0,80,0.0158689990571298,0.012420166728756165,-2.6175337424784776e-6,0.011170690851616599,0.9808333333333333,0.9972457627118644,0.6150417067919816,0.0,,1.0,1.7581250101327897,0.0003074568836269114,0.000040273987420940435,0.00020226657807477985,0.0006695612266902239 | |
| 37 | +baseline,S_coarseloc,province,1.0,80,0.018753905217264135,0.014579574565887054,0.0000927790924799411,0.013394088251267455,0.9804166666666667,0.9955508474576271,0.5612967611671359,0.0,,1.0,2.652030420303345,0.0004443467958474379,0.00005018300298875126,0.0003198813091666877,0.0007124939854657648 | |
| 38 | +baseline,E_fisher_chained,province,1.0,80,0.021149408252854322,0.01642836728470508,2.0218299880615156e-6,0.0180165930755218,,,0.5110613664939041,0.0125,,1.0,5.106732937693596,0.00047098617051218397,0.00007112097543465064,0.0005194747434755314, | |
| 39 | +baseline,B_stratified,province,1.0,80,0.030775052536997555,0.024056527458482364,0.0002377177901692239,0.022174154759761346,,,0.38997229106569364,0.05,,1.0,0.02190931439399719,0.0007200383897527102,0.00008166933162587893,0.0005253135422176184, | |
| 40 | +baseline,A_median,province,1.0,80,0.03561022392223612,0.027962339142070015,0.00009881295631894466,0.025596197429553758,,,0.3415993257106822,0.1375,,1.0,0.0073881596326828,0.0007938983458593675,0.00010582763603244672,0.0005671147447246734, | |
| 41 | +baseline,H_repeat_sales,province,1.0,80,0.05322541043985056,0.04095704364236959,-0.00006682526068718409,0.0372621726499552,,,0.2164686276115007,-0.025,,1.0,0.058115845918655394,0.0009941041167210183,0.00016289973753657325,0.0006939940820446875, | |
| 42 | +baseline,K_qhpi,region,4.0,80,0.01172220863326712,0.009385507129555517,-0.00006387798837239349,0.011112782795942067,0.9355208333333334,0.9820974576271186,0.5887996705298236,-0.021875,,1.0,,0.00011500258950488282,0.00002424770081629551,0.00012125847643879751,0.00222485436042575 | |
| 43 | +baseline,S_LLT,region,4.0,80,0.013907890377218268,0.011011313910702468,0.00002162501072437644,0.012131835792265951,0.9836458333333333,0.9983580508474577,0.6423694067722521,0.00625,0.00817156627000645,1.0,,0.0002192987215775729,0.00003657930639221806,0.00016998226745097372,0.001176119686011074 | |
| 44 | +baseline,S_noEB,region,4.0,80,0.013941926087476176,0.011042028143162637,0.000016995077306195397,0.012082799771049379,0.9838020833333333,0.9985699152542373,0.6422665310721158,0.003125,0.005264082740635426,1.0,,0.00022005347356403404,0.00003535097928792445,0.0001668189893862845,0.0011338284386017763 | |
| 45 | +baseline,S_sharp,region,4.0,80,0.013989786869140841,0.011079688385874715,0.000015211994380267537,0.01208605621950493,0.98703125,0.9989406779661018,0.6434850862151739,0.003125,0.005868524630210708,1.0,,0.00022104569117695368,0.00003537823513868461,0.0001660361965499059,0.0009433966864097917 | |
| 46 | +baseline,S_noTV,region,4.0,80,0.013993298459806656,0.01108031813633217,4.921046085858313e-6,0.012076648752689881,0.9869791666666666,0.9988877118644067,0.6433175122455891,0.003125,0.0058711030967631295,1.0,,0.00022079583270683,0.00003507209689586514,0.00016530641074370166,0.0009474101152679092 | |
| 47 | +baseline,S_sharp_filtered,region,4.0,80,0.015791871531900557,0.012554474204668115,0.000025809514007279112,0.013958074654676573,0.9845312500000001,0.9978813559322035,0.6076518798244609,0.00625,,1.0,,0.00024054389502932105,0.00003754482045391547,0.00020702574612419135,0.0009949816669129995 | |
| 48 | +baseline,S_norobust,region,4.0,80,0.016973232172239106,0.01336219813802938,7.452014072557975e-6,0.013607381289341528,0.990625,0.998728813559322,0.5670088619516698,0.00625,0.005966470017251005,1.0,,0.00028384431166480546,0.00004144397810413798,0.00019752687418720085,0.0007931365435110185 | |
| 49 | +baseline,S_coarseloc,region,4.0,80,0.019696544314659357,0.015394533821234586,0.00010157310690530846,0.01595836105868235,0.9884375000000001,0.9962923728813559,0.5125452674801534,0.003125,0.006069651845100096,1.0,,0.00041866419604015575,0.00005176093233147738,0.0003019543903560494,0.0009998873661954937 | |
| 50 | +baseline,C_pooled_td,region,4.0,80,0.026292746989195814,0.02047792181374269,0.000025578849888536705,0.018640569930321538,0.9795833333333333,0.9953389830508474,0.4725966338132389,-0.0125,,1.0,,0.00039998088338834715,0.00003567175348963209,0.00027761252421387863,0.00041666666666666637 | |
| 51 | +baseline,D_rtd13,region,4.0,80,0.029905351032948547,0.023235662315547915,9.691230600237133e-7,0.021460267851450512,,,0.4321712013677634,-0.028125,,1.0,,0.0004789058884180158,0.0000428798682485681,0.00033341801720700246, | |
| 52 | +baseline,B_stratified,region,4.0,80,0.06028337825480438,0.04712824244317552,0.00010107795650242946,0.04315530465171589,,,0.23581365004835905,-0.03125,,1.0,,0.001328560491816101,0.00008437636621287402,0.0009300010545577075, | |
| 53 | +baseline,A_median,region,4.0,80,0.07242324664816283,0.056500341759534,0.0001083012842114878,0.05137977772365278,,,0.2096089075152149,0.14375,,1.0,,0.0015669153082677658,0.00009534283339530116,0.0010728600606646581, | |
| 54 | +baseline,H_repeat_sales,region,4.0,80,0.12030898678394238,0.09120194156483111,0.000059245629345612075,0.08476925913096241,,,0.1148396802700953,-0.046875,,0.9985416666666668,,0.002056634438749038,0.0001921184793753776,0.0014152679659144211, | |
| 55 | +drifting_hedonics,S_LLT,mrc,12.0,80,0.014933586400817886,0.011823327843310753,0.00003819536202011584,0.015606063563900357,0.9729513888888889,0.9986935028248588,0.6079845806167948,0.004166666666666667,0.01846777991334935,1.0,,0.00022262163261139108,0.00004003690644356069,0.00018457828750330352,0.0012535051554868047 | |
| 56 | +drifting_hedonics,S_noTV,mrc,12.0,80,0.015007296090848766,0.011895664290418533,0.000025259788985313548,0.015260243751561573,0.9815972222222221,0.9994350282485875,0.6092859718439503,0.004166666666666667,0.011265244323077355,1.0,,0.00021917556817270428,0.00003628341197028749,0.00017394511046509592,0.000950666451405755 | |
| 57 | +drifting_hedonics,S_sharp,mrc,12.0,80,0.015025588585792287,0.011909756395736026,0.00003652973966367689,0.015314396199266963,0.9809548611111112,0.9994173728813559,0.6094434412150581,0.002083333333333333,0.01125822397350498,1.0,,0.00022047966138600197,0.00003877630118001664,0.00017557794356584045,0.0010079196348425903 | |
| 58 | +drifting_hedonics,S_noEB,mrc,12.0,80,0.0150599505694676,0.011930724217632253,0.00003629021307531722,0.01558532935859069,0.9736979166666666,0.9989053672316384,0.6070172261898122,0.0031249999999999997,0.010655471511443618,1.0,,0.00021896923260591466,0.000039137300754912885,0.00017868194609725412,0.0011978147442321138 | |
| 59 | +drifting_hedonics,S_norobust,mrc,12.0,80,0.018201428502887418,0.014336561020912719,0.000024704816732532346,0.016965679636586812,0.9875347222222223,0.9989406779661018,0.5360332641290534,-0.002083333333333333,0.012454822417463103,1.0,,0.00030378264143684683,0.000044136925672321136,0.00021677881548051785,0.0008309032635842203 | |
| 60 | +drifting_hedonics,K_qhpi,mrc,12.0,80,0.018716340914144987,0.014856102528420945,-0.00002500800398216896,0.01968298192220023,0.870329861111111,0.9588453389830509,0.5112016956277514,0.0010416666666666664,,1.0,,0.0003436267296866314,0.00003926279249786701,0.0003169507234412992,0.0038880916833763 | |
| 61 | +drifting_hedonics,S_sharp_filtered,mrc,12.0,80,0.01983923559003438,0.01547686053867844,0.000042374130914536506,0.02084743637919726,0.9700868055555555,0.9985699152542372,0.5218867395847656,0.04375,,1.0,,0.000259401604561534,0.00005089514647672392,0.0002707164171975279,0.001135979975061539 | |
| 62 | +drifting_hedonics,S_coarseloc,mrc,12.0,80,0.020526109658039176,0.01609920256010217,0.0001167979914199842,0.01879034572689095,0.986736111111111,0.99795197740113,0.4875391857018898,0.019791666666666666,0.013145972926322216,1.0,,0.00041840462045882623,0.00005472807161006104,0.0002832502633273142,0.0010066185393344546 | |
| 63 | +drifting_hedonics,C_pooled_td,mrc,12.0,80,0.05610827486229525,0.042970508649287496,0.000017404258907064073,0.03989811557805244,0.9781537556925967,0.9938475761442842,0.2750557836765752,-0.01666666666666667,,0.9993055555555556,,0.0009093800312839138,0.000055869364398497586,0.0006415784141874058,0.0003163273389810838 | |
| 64 | +drifting_hedonics,D_rtd13,mrc,11.2875,80,0.06144989635332859,0.047126629434841805,0.000027967596630434407,0.044345478840770315,,,0.25578120284055494,-0.020492424242424243,,0.9998892045454545,,0.0009132425895642549,0.0000596353646580254,0.0006404717633391024, | |
| 65 | +drifting_hedonics,B_stratified,mrc,12.0,80,0.08708430264949041,0.06848311637173236,0.000122944948138233,0.06703564185259864,,,0.16156136207370525,0.04583333333333334,,0.9969270833333332,,0.0009309247804547393,0.00007651717649064355,0.000731757993927863, | |
| 66 | +drifting_hedonics,A_median,mrc,12.0,80,0.13591763562897757,0.10559839986214996,-0.000052239119816791416,0.09647846525489683,,,0.12123718183483992,0.026041666666666664,,0.9993055555555556,,0.0020288272493625046,0.00012255670564198794,0.0014430262027587068, | |
| 67 | +drifting_hedonics,H_repeat_sales,mrc,12.0,80,0.2534170512636312,0.19252431290497668,-0.0002928447793687318,0.18049269566287612,,,0.062175020476940866,-0.07062500000000001,,0.9325520833333334,,0.003461498843725904,0.0004228150797980234,0.0025384498515032086, | |
| 68 | +drifting_hedonics,S_noTV,municipality,60.0,80,0.016140825504818417,0.012793175740464749,0.000023060358126695945,0.02272072838587029,0.9841006944444445,0.9998622881355932,0.5831570373271873,0.008125,0.024007761812999014,1.0,,0.0002241162915598591,0.000039050156003903216,0.00033159919397834816,0.0005623538807193423 | |
| 69 | +drifting_hedonics,S_sharp,municipality,60.0,80,0.016157953860650276,0.012808005020191148,0.000037222861728833246,0.02277808145852521,0.9840972222222222,0.999848163841808,0.5831153948765595,0.008333333333333333,0.02394420193799817,1.0,,0.000220395408768918,0.00004117301831585037,0.000330358313516289,0.0005899700322949433 | |
| 70 | +drifting_hedonics,S_LLT,municipality,60.0,80,0.0164818242367902,0.013046776223293071,0.00002575390533285596,0.02919056590561226,0.9672499999999999,0.9997492937853106,0.578003197292148,0.008958333333333334,0.04605527341314554,1.0,,0.00023540606186967455,0.00004695543178531761,0.0005591654321951424,0.0009393113589411062 | |
| 71 | +drifting_hedonics,S_noEB,municipality,60.0,80,0.016571718697200945,0.01311958996771059,0.00003214067578102394,0.02432650776139639,0.9752847222222222,0.9996680790960453,0.5759392467170428,0.010833333333333334,0.023998068548201378,1.0,,0.00021727231541742998,0.00004294166631004253,0.0003379843554091862,0.0007675636422814285 | |
| 72 | +drifting_hedonics,S_coarseloc,municipality,60.0,80,0.020831684423434672,0.016359667272674433,0.00011353237692785886,0.021276333188271077,0.9940972222222222,0.9995974576271186,0.4745114916793084,0.022083333333333333,0.026811677515299302,1.0,,0.0004104763925701687,0.0000527139598957565,0.0002460360248109461,0.00035662377442754795 | |
| 73 | +drifting_hedonics,S_norobust,municipality,60.0,80,0.02286610512854351,0.017673749590360572,0.00004029225296735394,0.030946251082401448,0.97921875,0.9983439265536724,0.48675723063872683,0.000833333333333334,0.030722171629658658,1.0,,0.0003853163536594985,0.00004997853497592007,0.0005751762719108741,0.0007023302915869578 | |
| 74 | +drifting_hedonics,K_qhpi,municipality,60.0,80,0.0292997323573652,0.022314732716049945,0.000011636478282642676,0.043129774016250386,0.8854861111111111,0.9760522598870056,0.4514836734150805,0.009375,,1.0,,0.0004538579294110938,0.000049219716426519784,0.0006073698913204983,0.0021531520476799084 | |
| 75 | +drifting_hedonics,S_sharp_filtered,municipality,60.0,80,0.0306396744347208,0.021033463177986007,0.000020070287479883563,0.040336075167946885,0.9689097222222222,0.9990466101694915,0.39478828764027973,0.11374999999999999,,1.0,,0.00037174345480134807,0.00006407608473591723,0.0005382627517413537,0.0007305784671502049 | |
| 76 | +drifting_hedonics,B_stratified,municipality,49.1875,80,0.06252944742536262,0.04697990797061992,-0.00011806296183286503,0.06791712821348901,,,0.10064257300983441,0.40673471989465737,,0.9677539796315363,,0.00019521163680948395,0.000034924849354073686,0.00025306750552910463, | |
| 77 | +drifting_hedonics,D_rtd13,municipality,11.8375,80,0.0775348944525367,0.058829090319670194,0.00001936548916548986,0.056078254856513784,,,0.20749578408177513,0.03491553075192781,,0.9994519916188114,,0.001004873643271412,0.00006379791708082826,0.0006870828953288087, | |
| 78 | +drifting_hedonics,C_pooled_td,municipality,39.2625,80,0.13008907446055387,0.09619401535488481,-0.0001869660845145535,0.09333010503736816,0.967406830354473,0.9830974202460856,0.14422548871713123,0.03142848608670387,,0.9565532957898307,,0.0010607226223301092,0.00012462991510127868,0.0007244002861375883,0.00038824285568607686 | |
| 79 | +drifting_hedonics,A_median,municipality,60.0,80,0.25890840363609613,0.20300111905940715,-0.0004032958515652055,0.1843951592567569,,,0.06915236781859804,-0.004481441103235918,,0.815420138888889,,0.0016920281846231691,0.0006983893701522075,0.0012110067927368245, | |
| 80 | +drifting_hedonics,H_repeat_sales,municipality,60.0,80,0.39490367628555795,0.3071461232883318,0.00011693468501631536,0.278372406424536,,,0.03307895347344812,-0.06091347210737099,,0.3024444444444444,,0.0045270271196300215,0.0008717471597270759,0.002907865877449769, | |
| 81 | +drifting_hedonics,C_pooled_td,province,1.0,80,0.012856680573567278,0.010065734222214063,5.133365172597718e-6,0.009117037507858482,0.9806250000000001,0.9976694915254237,0.6950400792008957,0.0,,1.0,0.2633653312921524,0.0002558055970626369,0.00003290272194793188,0.00016552350811530352,0.0007526179111026486 | |
| 82 | +drifting_hedonics,S_noEB,province,1.0,80,0.012913146925384294,0.010104422430042066,0.000012035574364777838,0.009181867837030315,0.9804166666666665,0.9972457627118644,0.693471938486393,0.0,,1.0,1.8842425018548965,0.00025836272081680023,0.00003485469062436159,0.00016647740021235363,0.0008267142187221701 | |
| 83 | +drifting_hedonics,S_LLT,province,1.0,80,0.012913146925384294,0.010104422430042066,0.000012035574364777838,0.009181867837030315,0.9804166666666665,0.9972457627118644,0.693471938486393,0.0,,1.0,13.917702892422676,0.0002583627208168001,0.00003485469062436159,0.0001664774002123537,0.0008267142187221711 | |
| 84 | +drifting_hedonics,S_sharp,province,1.0,80,0.012913146925384294,0.010104422430042066,0.000012035574364777838,0.009181867837030315,0.9804166666666665,0.9972457627118644,0.693471938486393,0.0,,1.0,2.1562725841999053,0.0002583627208168,0.00003485469062436159,0.00016647740021235376,0.0008267142187221711 | |
| 85 | +drifting_hedonics,S_noTV,province,1.0,80,0.012916446479547844,0.010108390287134141,4.505837111650115e-6,0.009155240915446925,0.9806250000000001,0.9972457627118644,0.6932822132302857,0.0,,1.0,2.160805341601372,0.00025730899940606006,0.000032986525850601844,0.00016662188519397944,0.0007526179111026471 | |
| 86 | +drifting_hedonics,S_sharp_bottomup,province,1.0,80,0.013075496166354481,0.010230057280847573,0.00002097806081753424,0.009564789117736418,,,0.6892288717981454,0.0,,1.0,,0.0002566495087518857,0.000036999001956660266,0.0001731849143965195, | |
| 87 | +drifting_hedonics,D_rtd13,province,1.0,80,0.014441891254509624,0.011272058137636506,-0.000017642482218618848,0.010470054588753639,,,0.6559833028670518,0.0,,1.0,5.011359548568725,0.00028910263817257225,0.00004081846289163924,0.00019992709985307226, | |
| 88 | +drifting_hedonics,K_qhpi,province,1.0,80,0.014441891254509624,0.011272058137636506,-0.000017642482218618845,0.010470054588753639,,,0.6559833028670518,0.0,,1.0,5.221142759919166,0.0002891026381725723,0.00004081846289163924,0.00019992709985307226, | |
| 89 | +drifting_hedonics,S_norobust,province,1.0,80,0.016329316908025013,0.012757471474806856,-8.745624502147421e-7,0.011509847501096255,0.9816666666666667,0.9976694915254237,0.605364470893148,0.0,,1.0,1.330391675233841,0.00032157361140031023,0.00004243001643050709,0.00020623079908912127,0.0005625439504630092 | |
| 90 | +drifting_hedonics,S_coarseloc,province,1.0,80,0.019166563712004266,0.014930591117965783,0.00009508788171549713,0.013725510142304425,0.9799999999999999,0.9959745762711865,0.5534973560455296,0.0,,1.0,2.18038272857666,0.0004515530847841988,0.000052591767903035496,0.0003188985593159152,0.0007500586006173473 | |
| 91 | +drifting_hedonics,E_fisher_chained,province,1.0,80,0.021481017344032786,0.01673660205210789,4.3525185918639775e-6,0.018406880950327015,,,0.5064001930802254,0.025,,1.0,4.158439293503761,0.0004760187513648588,0.00007834891805394499,0.000547010822426422, | |
| 92 | +drifting_hedonics,B_stratified,province,1.0,80,0.03141178995643008,0.02461124445985117,0.00024246596437999717,0.022662439124023243,,,0.3787144896119051,0.025,,1.0,0.012393131852149963,0.0007081045624712546,0.00008835041692591188,0.0005268652402914988, | |
| 93 | +drifting_hedonics,A_median,province,1.0,80,0.036913016315274494,0.028927141985484385,0.00009883604416127975,0.026499312315136258,,,0.33220186170059973,0.0625,,1.0,0.004426434636116028,0.0007760647873162525,0.00011082944398687254,0.0005620342923295426, | |
| 94 | +drifting_hedonics,H_repeat_sales,province,1.0,80,0.053725334236477726,0.04134290438307492,-0.00004023894391801468,0.0375576445892207,,,0.21624186807341306,0.0125,,1.0,0.04624057710170746,0.0009924910513125049,0.00015885682090214978,0.0006885989253872923, | |
| 95 | +drifting_hedonics,K_qhpi,region,4.0,80,0.011860487124308306,0.009502313282298537,-0.00006560836003295626,0.011383431031752166,0.9333333333333333,0.9819385593220339,0.5789384288563639,0.0,,1.0,,0.00011802478936720022,0.000026218299058577336,0.00011945815635765228,0.0022628364884094174 | |
| 96 | +drifting_hedonics,S_LLT,region,4.0,80,0.014313357758936043,0.01131775713597941,0.000027703715366336127,0.012467717270831744,0.9828125,0.9979343220338983,0.6313649354325191,0.00625,0.008190787898853727,1.0,,0.00022828749683513695,0.000038477809047317196,0.000175516760947679,0.0011371699726800374 | |
| 97 | +drifting_hedonics,S_noEB,region,4.0,80,0.01434825282339835,0.011344717867232434,0.00002334100972527177,0.012420851566785367,0.9828125,0.9984110169491526,0.6313654339616747,0.00625,0.005309017603691669,1.0,,0.00022782543811107825,0.0000373666809658557,0.00017101463151145444,0.0011775202458237824 | |
| 98 | +drifting_hedonics,S_noTV,region,4.0,80,0.014386584225770818,0.011375294513843835,0.000013807054434477693,0.012382253480412792,0.9866145833333334,0.998781779661017,0.6322883546385104,0.003125,0.005902753575726048,1.0,,0.00022672551546238555,0.000035472792703880735,0.00016718316190023627,0.0009598506322334846 | |
| 99 | +drifting_hedonics,S_sharp,region,4.0,80,0.014393032153295956,0.01137628983618979,0.000023335388539312854,0.012402120078034684,0.9860416666666667,0.998781779661017,0.6327374961156778,0.003125,0.005924899403550115,1.0,,0.00022854476542875284,0.000037395467560547615,0.00016833206137934785,0.0009864032569759293 | |
| 100 | +drifting_hedonics,S_sharp_filtered,region,4.0,80,0.016173807468670302,0.012863048267369523,0.00003434426507535705,0.014304389353906852,0.9839583333333334,0.9980932203389831,0.5983401673520972,0.00625,,1.0,,0.00024430615295271166,0.000039816486231087206,0.00020720086070701233,0.0010379681174966428 | |
| 101 | +drifting_hedonics,S_norobust,region,4.0,80,0.017409347087715025,0.013683796825753675,7.069279469954756e-6,0.013943053441659397,0.990625,0.9981991525423728,0.5576819538173765,0.00625,0.00605361541615365,1.0,,0.0002961810469758783,0.00004336903645815779,0.00019961984252489596,0.000757710598855158 | |
| 102 | +drifting_hedonics,S_coarseloc,region,4.0,80,0.020091471824788192,0.015735390795035296,0.00010631434021403458,0.01624030065401893,0.9878645833333334,0.9964512711864406,0.5052380770247777,0.021875,0.006080113449399642,1.0,,0.0004254122456331653,0.000053765628803898515,0.00030208429277163445,0.0010643463264297008 | |
| 103 | +drifting_hedonics,C_pooled_td,region,4.0,80,0.02728050462105553,0.021269221231844562,0.000031601064197593284,0.019366296088497267,0.9795833333333333,0.9953389830508474,0.4604709211941228,-0.003125,,1.0,,0.0004254616209361352,0.00003774708579094786,0.00029093950219229127,0.0004100208392357565 | |
| 104 | +drifting_hedonics,D_rtd13,region,4.0,80,0.03098609903437774,0.024051509668859668,0.000017710521478680385,0.02228298721812124,,,0.4215121509868651,-0.03125,,1.0,,0.0005045262333356912,0.00004481046498041412,0.00034609912136474176, | |
| 105 | +drifting_hedonics,B_stratified,region,4.0,80,0.06180478534529808,0.04831192976014545,0.00013417441899219183,0.04421301005584989,,,0.23282542819205204,0.059375,,1.0,,0.001366520917598163,0.00008501733875885769,0.0009482105013325094, | |
| 106 | +drifting_hedonics,A_median,region,4.0,80,0.07557899884941924,0.0588919515345833,0.0001248820610754362,0.05354232727510592,,,0.2016434768332261,0.1125,,1.0,,0.001593648253959007,0.00009987201177658053,0.0010873089209241507, | |
| 107 | +drifting_hedonics,H_repeat_sales,region,4.0,80,0.12175673418787034,0.09222809183307286,0.00006769180808444896,0.08579574603358137,,,0.11453914121091333,-0.040625,,0.9985416666666668,,0.0020948663714549136,0.00018918809928272266,0.0014469119969375802, | |
| 108 | +flat_quality_up,S_LLT,mrc,12.0,80,0.014575005467902464,0.011550281884122658,0.0005181565045834326,0.017352570971533653,0.9473090277777777,0.9988170903954803,0.3665028912292496,0.01979166666666667,0.018331927820099087,1.0,,0.00021404381185728674,0.000037716646153108845,0.00019351529411326383,0.0023921829992753067 | |
| 109 | +flat_quality_up,S_sharp,mrc,12.0,80,0.014664755071516797,0.011633548093887745,0.0005157652361831634,0.017121179781161993,0.9598611111111112,0.9994526836158192,0.3755784104265881,0.02708333333333333,0.011090462065190099,1.0,,0.0002157654702983119,0.0000372202436695159,0.00019305652529744147,0.002078434230333083 | |
| 110 | +flat_quality_up,S_noTV,mrc,12.0,80,0.014670269094116301,0.011636774563943847,0.0004764083221048371,0.01680463515682051,0.9629687499999999,0.9994173728813559,0.37553179344508314,0.023958333333333335,0.011096980576184256,1.0,,0.0002154148468755229,0.00003639463339637783,0.0001860984471664509,0.0018373179548727054 | |
| 111 | +flat_quality_up,S_noEB,mrc,12.0,80,0.014678467691307321,0.011641036549681908,0.0005165103001086492,0.017323315665392067,0.9495486111111111,0.9989759887005649,0.36905715995150057,0.03125,0.010503867433320626,1.0,,0.00021343020058292383,0.000037487013641653835,0.00019350321808029108,0.002300264499414866 | |
| 112 | +flat_quality_up,K_qhpi,mrc,12.0,80,0.0174369214485059,0.013839759130862841,0.0004625849061332287,0.0202286952094237,0.8301909722222222,0.9608757062146893,0.3422346789310859,0.014583333333333334,,1.0,,0.0003764260695977708,0.0000370991222793977,0.00032760246773284705,0.004208630670931643 | |
| 113 | +flat_quality_up,S_norobust,mrc,12.0,80,0.017730456596101837,0.013987172007073787,0.0005099865054266533,0.018513327700649864,0.9766840277777777,0.9990642655367232,0.31612677390855093,-0.028125,0.012268564696421461,1.0,,0.0002910474249548176,0.000043544570841315246,0.0002359241977899311,0.001464972528296506 | |
| 114 | +flat_quality_up,S_sharp_filtered,mrc,12.0,80,0.019416195237451424,0.015108395971396103,0.0005230998839691641,0.022125972933465715,0.9527777777777777,0.9986935028248587,0.3261260448609317,0.253125,,1.0,,0.00026080903711947576,0.00004931236342074939,0.0002708464712047322,0.0017562996362386137 | |
| 115 | +flat_quality_up,S_coarseloc,mrc,12.0,80,0.020185511972220106,0.01580475936004041,0.000469455746064617,0.019689889009393554,0.9808680555555556,0.9979166666666666,0.2680619881607593,0.084375,0.013081114663951222,1.0,,0.0004148234374012201,0.00005132069701208801,0.00027527631720176206,0.0013906403776908225 | |
| 116 | +flat_quality_up,C_pooled_td,mrc,12.0,80,0.05416310768313769,0.041504025729266106,0.00048518331678380126,0.03928783953770422,0.9778954720204194,0.9939343801126046,0.19942273204343003,0.0375,,0.9993055555555556,,0.0008558450884669764,0.000053667388766997605,0.0006020387803579078,0.00034753696513574516 | |
| 117 | +flat_quality_up,D_rtd13,mrc,11.2875,80,0.059320522792571884,0.04559297299954956,0.0004720119258294018,0.043557493760628334,,,0.18539292177409283,0.030997474747474753,,0.9998892045454545,,0.0008546932547556947,0.00005889787691481985,0.000601502633419832, | |
| 118 | +flat_quality_up,B_stratified,mrc,12.0,80,0.0855155735099565,0.06748391314924562,0.0012206411937465806,0.06769609999433347,,,0.11353484605050686,0.032291666666666656,,0.996857638888889,,0.0008466360709046435,0.0000711341075033639,0.0006691632659328607, | |
| 119 | +flat_quality_up,A_median,mrc,12.0,80,0.13167700158128134,0.10222688785212113,0.0025314893972618133,0.10432381272507636,,,0.08557206928144795,0.031249999999999993,,0.9993055555555556,,0.0020341483845536844,0.00012054964891228727,0.0013653782098758235, | |
| 120 | +flat_quality_up,H_repeat_sales,mrc,12.0,80,0.2512862831153212,0.19077805887839644,0.00021510422483699736,0.17904999021506904,,,0.045496012585196,0.032897727272727266,,0.9325520833333334,,0.0034411992378512216,0.000426011459824531,0.002514764015255907, | |
| 121 | +flat_quality_up,S_noTV,municipality,60.0,80,0.01581675454504837,0.012541012353263862,0.00047863684295666124,0.02362471604305807,0.9757604166666667,0.9998764124293785,0.34985442871140926,0.006666666666666665,0.02366120836976149,1.0,,0.00021510783501579205,0.00003981018191937925,0.0003254152016048525,0.0008048754305205567 | |
| 122 | +flat_quality_up,S_sharp,municipality,60.0,80,0.015820145597904595,0.012543629883344277,0.0005191684495481783,0.023873906610580937,0.9743437500000001,0.9998764124293785,0.34979268655856605,0.005416666666666664,0.023659527652167642,1.0,,0.00021478963482660655,0.000040588236767359894,0.0003256077855889051,0.0008913282356931948 | |
| 123 | +flat_quality_up,S_LLT,municipality,60.0,80,0.01613451130730014,0.012772899254787107,0.0005263685222047455,0.029627953529311885,0.9567708333333333,0.9997210451977401,0.3387551411147281,0.02,0.04571457221117354,1.0,,0.00021814997046748312,0.000046113156489659004,0.00044219224345416416,0.001263371741233712 | |
| 124 | +flat_quality_up,S_noEB,municipality,60.0,80,0.016201514184896612,0.012834796045274096,0.0005198514479974458,0.02540769459051735,0.9625347222222222,0.9997175141242938,0.3405182100618009,0.024375,0.02375165169262444,1.0,,0.000212825581537125,0.00004204608048518023,0.00034030163284994876,0.0011740729082546299 | |
| 125 | +flat_quality_up,S_coarseloc,municipality,60.0,80,0.02049408238592089,0.016066890043360427,0.00046493603079950373,0.02190842001926904,0.9913819444444444,0.9995692090395479,0.25451368193568935,0.059583333333333335,0.026717361997212686,1.0,,0.00040672000544200607,0.00004911712716565972,0.00024178551614435575,0.0005405627929359709 | |
| 126 | +flat_quality_up,S_norobust,municipality,60.0,80,0.02242909816104009,0.01731709646439087,0.0005265156752676242,0.031571079536939925,0.9730659722222222,0.9982697740112995,0.28114609506768634,-0.009375,0.030306607087349575,1.0,,0.00036704221374698505,0.000050757981597825086,0.0005405686593269746,0.0008939160398060083 | |
| 127 | +flat_quality_up,K_qhpi,municipality,60.0,80,0.029038194271302532,0.022046520840905415,0.0004557407390030034,0.043283260272054695,0.8733923611111111,0.9753531073446327,0.25704447250944257,0.03125,,1.0,,0.0004593447996213154,0.0000476785747325233,0.000614913532137367,0.002250575611754056 | |
| 128 | +flat_quality_up,S_sharp_filtered,municipality,60.0,80,0.030183348400323447,0.0206207615425099,0.0005013808663112809,0.04077448560638868,0.9607465277777777,0.9989406779661018,0.23527831652919867,0.3408333333333333,,1.0,,0.0003648994553691111,0.00006369992190889215,0.000528108905008541,0.0008213893483280179 | |
| 129 | +flat_quality_up,B_stratified,municipality,49.1875,80,0.0600091698449491,0.04470069945347466,0.0013598953531118987,0.06757846139516413,,,0.07293871956065338,0.27621880810152305,,0.9671073516333729,,0.00017010826880628304,0.00003598304880174054,0.00024417007166732446, | |
| 130 | +flat_quality_up,D_rtd13,municipality,11.8375,80,0.07497265467048134,0.05689403385005444,0.00048112534786046775,0.05476435667792946,,,0.15487831646917866,0.015235473268561506,,0.9994519916188114,,0.0009702906370895096,0.00005861135286032018,0.0006646146158414721, | |
| 131 | +flat_quality_up,C_pooled_td,municipality,39.2625,80,0.12548725360337756,0.09268628028332708,0.000287701902779297,0.0904680415428529,0.9675129335423408,0.9830954594601717,0.10585172278278587,0.003661657978858145,,0.9565275781612005,,0.0009948064530957517,0.00012045623020083642,0.0006780679880453648,0.00040871851106046394 | |
| 132 | +flat_quality_up,A_median,municipality,60.0,80,0.24714410422938576,0.19358022175803913,0.002112599741389073,0.1822366915342712,,,0.049915502214177856,-0.0159618783594579,,0.815420138888889,,0.001632769334278066,0.0006726051496176098,0.001126813126323775, | |
| 133 | +flat_quality_up,H_repeat_sales,municipality,60.0,80,0.39076959559427515,0.30357593619692885,0.0012018599505921482,0.2750418109169931,,,0.02632588672990884,-0.04571414889772059,,0.3024444444444444,,0.004448545260965997,0.0008906711798791479,0.0028479232842806472, | |
| 134 | +flat_quality_up,C_pooled_td,province,1.0,80,0.012498468839534638,0.009789938198445858,0.00044668774650899544,0.011655406433412573,0.9658333333333333,0.9972457627118644,0.49397705067392794,0.0125,,1.0,0.29522075355052946,0.00024697824602810747,0.000032250818821939185,0.00017094474007466366,0.0022754267308163607 | |
| 135 | +flat_quality_up,S_noEB,province,1.0,80,0.01254926770965275,0.009832962479480987,0.0004860114995671581,0.012143809696581177,0.961875,0.9974576271186442,0.49241642239587824,0.0125,,1.0,2.1120935797691347,0.0002483060005390625,0.00003281181715048013,0.0001773110523152792,0.0026217497123388967 | |
| 136 | +flat_quality_up,S_sharp,province,1.0,80,0.01254926770965275,0.009832962479480987,0.0004860114995671581,0.012143809696581177,0.961875,0.9974576271186442,0.49241642239587824,0.0125,,1.0,2.351738527417183,0.00024830600053906256,0.00003281181715048012,0.00017731105231527924,0.002621749712338897 | |
| 137 | +flat_quality_up,S_LLT,province,1.0,80,0.01254926770965275,0.009832962479480987,0.0004860114995671581,0.012143809696581177,0.961875,0.9974576271186442,0.49241642239587824,0.0125,,1.0,14.073842906951905,0.00024830600053906245,0.000032811817150480116,0.00017731105231527915,0.002621749712338896 | |
| 138 | +flat_quality_up,S_noTV,province,1.0,80,0.012552081625253758,0.009830716805393746,0.0004462392599695683,0.011685277851553664,0.9658333333333333,0.9976694915254237,0.4921693543426399,0.0125,,1.0,2.3556492894887926,0.00024787121608527737,0.000032355216911320046,0.00017215923074947144,0.002275426730816363 | |
| 139 | +flat_quality_up,S_sharp_bottomup,province,1.0,80,0.012711569677738168,0.0099739563324376,0.000504517362772309,0.012639829033073844,,,0.48890741303333546,0.0125,,1.0,,0.00024750521062264234,0.000035486252243178526,0.00018465945138908675, | |
| 140 | +flat_quality_up,D_rtd13,province,1.0,80,0.013984232356032977,0.010912542483737664,0.00042187965611917256,0.012398868214443973,,,0.4590290453013969,0.025,,1.0,5.637413626909256,0.0002774622079523336,0.0000384904014859569,0.00022586889937789928, | |
| 141 | +flat_quality_up,K_qhpi,province,1.0,80,0.013984232356032977,0.010912542483737664,0.00042187965611917256,0.012398868214443973,,,0.4590290453013969,0.025,,1.0,6.085581922531128,0.00027746220795233365,0.0000384904014859569,0.00022586889937789936, | |
| 142 | +flat_quality_up,S_norobust,province,1.0,80,0.015925483084801493,0.01246313470750585,0.000488142309630044,0.014010371924461515,0.975,0.9972457627118644,0.4071280562510001,0.0,,1.0,1.5622018903493882,0.0003070628919636228,0.00004128033044681306,0.0002270079243943795,0.001482433472843374 | |
| 143 | +flat_quality_up,S_coarseloc,province,1.0,80,0.018802664691216026,0.014626449916093302,0.0004423410609253577,0.014913342364784133,0.974375,0.9957627118644068,0.3636411916397601,0.075,,1.0,2.360251745581627,0.0004431765720161608,0.000048197075866517556,0.0002939401643099925,0.001965412735845125 | |
| 144 | +flat_quality_up,E_fisher_chained,province,1.0,80,0.02119316044054611,0.016466674198746235,0.00030541999712445627,0.018663478113605227,,,0.3226263674566776,0.1125,,1.0,4.750209930539131,0.00047383218953660694,0.00007144115128709457,0.0005428549960333601, | |
| 145 | +flat_quality_up,B_stratified,province,1.0,80,0.03132538982550087,0.02446691618464284,0.0009048332679389476,0.025833994832282086,,,0.2243254920586153,0.05,,1.0,0.016469213366508483,0.0007840769305579662,0.00008819123213732172,0.0004807504811961176, | |
| 146 | +flat_quality_up,A_median,province,1.0,80,0.03615298791563451,0.02836402688719042,0.0026451177851272217,0.05139203578579983,,,0.2053062673549002,-0.05,,1.0,0.006906792521476746,0.0008012820209397555,0.00010510653802513332,0.0005659414909353969, | |
| 147 | +flat_quality_up,H_repeat_sales,province,1.0,80,0.053281890352314655,0.04102092494291171,0.00040824334853132167,0.038011116797823555,,,0.12292724673311932,-0.175,,1.0,0.05416384637355805,0.0009871657552375818,0.00016137957444100116,0.0006814302799694006, | |
| 148 | +flat_quality_up,K_qhpi,region,4.0,80,0.008399371533149812,0.006725377391364272,0.00045508176481877545,0.011819598264070964,0.8274479166666667,0.9858580508474576,0.4770329936005561,-0.028125,,1.0,,0.0000933099108980409,0.000019327918733215657,0.00012085132483155156,0.005498356417372176 | |
| 149 | +flat_quality_up,S_LLT,region,4.0,80,0.013971602344042076,0.011053428604514439,0.0005136036966652856,0.01483375134275168,0.9567708333333333,0.9983050847457626,0.3950824257939926,0.021875,0.008169046384901724,1.0,,0.00021997625400927285,0.00003701851906240512,0.00018190648465889363,0.002632493615447665 | |
| 150 | +flat_quality_up,S_noEB,region,4.0,80,0.014002799050965246,0.01108101258526809,0.0005074749234370931,0.014753950406036581,0.9561979166666668,0.9983580508474577,0.39613866253107477,0.03125,0.005254595832545063,1.0,,0.00021991103111637872,0.000036007588483166815,0.00018282502698630722,0.0030606002729618963 | |
| 151 | +flat_quality_up,S_sharp,region,4.0,80,0.014050591475687286,0.011120572795951592,0.0005057574700883295,0.014755304344981592,0.9628124999999998,0.9989406779661018,0.402671863019643,0.025,0.005868080306704639,1.0,,0.00022166291591684158,0.00003600852968260098,0.00018399403170832137,0.0025663391877638847 | |
| 152 | +flat_quality_up,S_noTV,region,4.0,80,0.014056868824576324,0.011123719052977167,0.0004665300912648009,0.014385275840602622,0.9666666666666666,0.998781779661017,0.4026068597204893,0.021875,0.005874302966840868,1.0,,0.00022120125547784482,0.000035446081129248806,0.00017734019444997276,0.002154634311309269 | |
| 153 | +flat_quality_up,S_sharp_filtered,region,4.0,80,0.015838727394077205,0.01258277119991881,0.0005165637582964361,0.01635658168061803,0.9588541666666666,0.9977754237288134,0.38705078200201387,0.1125,,1.0,,0.00024141358324207053,0.00003855773159371391,0.00021473994600048206,0.0023965211817576757 | |
| 154 | +flat_quality_up,S_norobust,region,4.0,80,0.017019767675438554,0.013400691186784225,0.0004987438933981806,0.016026232631108292,0.9789583333333333,0.998728813559322,0.33719225012408455,-0.015625,0.0059737189896648816,1.0,,0.0002844362476787127,0.000042424138405635645,0.0002141481405266696,0.001618672089067722 | |
| 155 | +flat_quality_up,S_coarseloc,region,4.0,80,0.019743903121769883,0.015437608829500371,0.000460502320405861,0.017314923987744632,0.9824479166666666,0.9963983050847458,0.28861673053512216,0.071875,0.00605997816054949,1.0,,0.00041798693278746063,0.00005005311301292501,0.00027828926196037226,0.0016750411800624694 | |
| 156 | +flat_quality_up,C_pooled_td,region,4.0,80,0.026392270669934888,0.02054762275171431,0.00046559719179225964,0.02027807574596729,0.9771354166666667,0.9953389830508474,0.33480925762145625,-0.028125,,1.0,,0.0004004516829870649,0.000035905522804777744,0.0002662154743463574,0.0005496698100248372 | |
| 157 | +flat_quality_up,D_rtd13,region,4.0,80,0.0300024184815224,0.023314216136179313,0.0004476650036503413,0.022803161655809694,,,0.30528481017473674,-0.0375,,1.0,,0.0004772194771722936,0.00004286737287678462,0.00032242818644785285, | |
| 158 | +flat_quality_up,B_stratified,region,4.0,80,0.06120395173100118,0.04796908859223576,0.000885650284496115,0.045870686790722176,,,0.15844132213992534,-0.05,,1.0,,0.0013071986206971693,0.00008339090905428406,0.0008829718345979095, | |
| 159 | +flat_quality_up,A_median,region,4.0,80,0.07336918082934675,0.05727102546719628,0.002668204122851717,0.06881488066727239,,,0.14002833911437615,0.0125,,1.0,,0.0015712376268228855,0.00009541540672538183,0.000914645032821732, | |
| 160 | +flat_quality_up,H_repeat_sales,region,4.0,80,0.12054210384609823,0.09136458640435853,0.0005330758974085366,0.0852451754634421,,,0.0814201810175825,0.03125,,0.9985416666666668,,0.002071960417282089,0.0001907510232120695,0.001417696364797446, | |
| 161 | +heavy_tails,S_coarseloc,mrc,12.0,80,0.024179349062337614,0.018872010071181056,8.671830871743954e-6,0.023961856852763405,0.97953125,0.9966984463276836,0.45326959503621084,0.004166666666666665,0.01833298975022588,1.0,,0.000623559721031931,0.00006049165242166973,0.0004630236297141011,0.0012849523566068179 | |
| 162 | +heavy_tails,S_noEB,mrc,12.0,80,0.03102534159662006,0.024614023880558243,-0.00005658629597745628,0.030420008802479824,0.9080729166666666,0.9715748587570621,0.4240998072918264,0.014583333333333332,0.018972341715300496,1.0,,0.0008269183752408565,0.000052385971826323985,0.000594326425400288,0.0019976358296216854 | |
| 163 | +heavy_tails,S_noTV,mrc,12.0,80,0.03182083007161886,0.025286331444127796,-0.00006714886722390619,0.03116007118195128,0.9085069444444445,0.973093220338983,0.4070711216315102,0.014583333333333332,0.01998806007746227,1.0,,0.0008010380242857862,0.000052122950980034154,0.0005752808886036323,0.0018730903634293266 | |
| 164 | +heavy_tails,S_sharp,mrc,12.0,80,0.03183050510109483,0.02529662608061183,-0.00005373777216449013,0.03117031018634602,0.9093402777777777,0.9732521186440678,0.40719455092265777,0.0125,0.01997713919963742,1.0,,0.0008025839530246409,0.00005190070966454508,0.0005738228354635413,0.0018796945644258586 | |
| 165 | +heavy_tails,S_LLT,mrc,12.0,80,0.03289696872755611,0.026066225388509917,-0.00004939699076287529,0.03267429785745006,0.8993923611111111,0.9697033898305085,0.4011597489669139,0.011458333333333334,0.026606108516323217,1.0,,0.0008486644381338662,0.00005320761852625683,0.0006049465886432666,0.0019862585990082743 | |
| 166 | +heavy_tails,S_norobust,mrc,12.0,80,0.03876934101876274,0.029944004549569452,-0.00003156489214648948,0.03515312239113187,0.9501041666666665,0.9799081920903955,0.3529310918748877,-0.017708333333333333,0.02278119516392887,1.0,,0.0009813381006262627,0.0000614759327888442,0.0007202193705816389,0.0014821812317996252 | |
| 167 | +heavy_tails,K_qhpi,mrc,12.0,80,0.0430159356664825,0.03318385924594415,-0.00005418688968358976,0.037549319597622796,0.805,0.8690677966101695,0.3462093127363309,-0.051041666666666666,,1.0,,0.0010611483343220462,0.00004902134537382439,0.0008097971905696369,0.0038786880226029836 | |
| 168 | +heavy_tails,S_sharp_filtered,mrc,12.0,80,0.04669177242377338,0.03693690768822795,-0.0001056937862307805,0.039663467073492345,0.8739409722222222,0.9254237288135594,0.3069918643674071,0.04583333333333334,,1.0,,0.0009526993583392686,0.00006489749172504206,0.0006789820893998668,0.001992763451239344 | |
| 169 | +heavy_tails,C_pooled_td,mrc,12.0,80,0.05490067301620709,0.04103354544856513,-0.0000569741191474516,0.03882999349217675,0.9798317389516221,0.9952380921540712,0.29712704502785486,-0.029166666666666667,,0.9992881944444445,,0.0010551573617858221,0.00006030363125161451,0.0007501991824850121,0.00026468699666897655 | |
| 170 | +heavy_tails,D_rtd13,mrc,11.3375,80,0.06196749139706109,0.04706333076883155,-0.00006343348497115826,0.04463572480255391,,,0.2605934867793689,0.011073232323232322,,0.9998702651515151,,0.0009909205630833037,0.0000524452850104771,0.000710583672557034, | |
| 171 | +heavy_tails,B_stratified,mrc,12.0,80,0.09684381647202675,0.07499360850352887,-0.000026294834149944307,0.07462180957701656,,,0.1415519084203297,0.015625,,0.9971701388888888,,0.0010721265716449237,0.0000832314524422864,0.000889434949115239, | |
| 172 | +heavy_tails,A_median,mrc,12.0,80,0.13354833786635084,0.10360059762001053,0.00013767958445971251,0.09466404353878681,,,0.12339008714333528,-0.0375,,0.9992881944444445,,0.002139826022848473,0.00013042310748368356,0.0015140560404989288, | |
| 173 | +heavy_tails,H_repeat_sales,mrc,12.0,80,0.328385194896656,0.24663005405339417,-0.0005608135208649022,0.2343213497517639,,,0.040899923493349755,0.012651515151515146,,0.9337152777777777,,0.005186104194834618,0.0006561260142300764,0.003522880748443071, | |
| 174 | +heavy_tails,S_coarseloc,municipality,59.9875,80,0.02842435096026789,0.022282391024398593,-0.000010334323787850528,0.03687600399637363,0.9783913017890772,0.997369230585081,0.40807818327432444,0.005829802259887007,0.03890308919714425,1.0,,0.0005914500037322656,0.00005786776150120926,0.0006841141456111662,0.001270093660229455 | |
| 175 | +heavy_tails,S_noEB,municipality,59.9875,80,0.04508335608135523,0.0352557764512708,-0.00007260774776861922,0.05615243224695214,0.907605461393597,0.9780418103035526,0.3335270745536067,-0.01611581920903955,0.04304836414390887,1.0,,0.0007860696563666457,0.00005589920479072507,0.0007076191328266301,0.001253967102005438 | |
| 176 | +heavy_tails,S_sharp,municipality,59.9875,80,0.04619943314304596,0.03625282484635564,-0.00005334612305956738,0.06000338045937954,0.9210304260828626,0.9828652566312363,0.3131868357724142,-0.009844632768361578,0.04799444260385705,1.0,,0.0007628341914642086,0.00005333444670320947,0.0006955871823173013,0.0012088074660110192 | |
| 177 | +heavy_tails,S_noTV,municipality,59.9875,80,0.04620295944066679,0.03625242431142482,-0.0000666879234396645,0.06001682566123785,0.9209503884180791,0.9828440103418558,0.3130310423966668,-0.009014830508474573,0.048026624538751506,1.0,,0.0007589650488743873,0.00005385768551647028,0.0006931442095552317,0.001207026900299527 | |
| 178 | +heavy_tails,S_LLT,municipality,59.9875,80,0.049504586272958846,0.0386469999581121,-0.00005019305449900244,0.0692044710632232,0.9013067914312618,0.9795212103801589,0.30156595519991847,-0.0061052259887005664,0.0716987005124576,1.0,,0.0007951340116348755,0.00006132104235087082,0.0008220279652808121,0.001370494082474302 | |
| 179 | +heavy_tails,S_norobust,municipality,59.9875,80,0.06771210219172961,0.05008708125859766,-0.000046168997738345227,0.07928008816571104,0.9191752589453861,0.9714204491046635,0.2375662495874979,-0.013806497175141241,0.06160100179884374,1.0,,0.0009389981287886334,0.00006725439962378391,0.0008927519911137111,0.001008176973600292 | |
| 180 | +heavy_tails,B_stratified,municipality,49.2,80,0.07600104932192438,0.05490107672658445,-0.00020991312722088714,0.08283000213745655,,,0.08645084651492375,0.31801111503427243,,0.968258714741222,,0.0003074350518442291,0.0000499463002028227,0.0003609152526383482, | |
| 181 | +heavy_tails,K_qhpi,municipality,59.9875,80,0.07888578229723565,0.05654695491905469,-0.00005484524859193832,0.0906907172027777,0.8331613700564973,0.9072239777841616,0.22009893646851522,-0.0021610169491525456,,1.0,,0.000701819149905635,0.000051638404761622605,0.000848949716552566,0.0012063510397243033 | |
| 182 | +heavy_tails,D_rtd13,municipality,11.85,80,0.0813680874918574,0.060184625859417494,0.00004249305664070912,0.05912275025915857,,,0.21291791477493716,0.07865640895052659,,0.9991998982260931,,0.0013383957557048908,0.00008396277180825631,0.000963195119049492, | |
| 183 | +heavy_tails,S_sharp_filtered,municipality,59.9875,80,0.08228510232405047,0.061152628062350146,-0.0001177508646189091,0.08238261066413899,0.8943536370056497,0.9461901512975199,0.19402917602254668,0.06458686440677966,,1.0,,0.001000763696112232,0.00007368317798364924,0.0008459604824132175,0.0012488964004931217 | |
| 184 | +heavy_tails,C_pooled_td,municipality,39.5375,80,0.1515228777976827,0.10321720941013199,-0.00015634869292539155,0.10830288025360357,0.9652547674459017,0.9777739062334814,0.1403250855288132,0.012741103517528895,,0.9555733760833499,,0.0016151982583825534,0.00016435898613357533,0.0011309007638391605,0.00033742981683243354 | |
| 185 | +heavy_tails,A_median,municipality,59.9875,80,0.2736989386636842,0.20685983660900073,0.0009293941750971773,0.1963452168683815,,,0.06731081890441792,0.003219394781153152,,0.8155314265536724,,0.0022454240774525277,0.0008292421973166006,0.001489166374495923, | |
| 186 | +heavy_tails,H_repeat_sales,municipality,59.9875,80,0.5170146863128174,0.39694186481670857,-0.002178137925134043,0.3663874937901529,,,0.027829306463394245,0.017034503528139615,,0.30142608286252354,,0.009924274538260343,0.0013505748555369544,0.006714190923565066, | |
| 187 | +heavy_tails,C_pooled_td,province,1.0,80,0.012416557426236177,0.00967183890017536,-0.000046694303057664776,0.008747347488143545,0.9824999999999999,0.9970338983050848,0.708503638386292,0.0,,1.0,0.2757605344057083,0.00023202466698927867,0.00003127965773059161,0.0001432865280677979,0.0005048986474166358 | |
| 188 | +heavy_tails,S_noTV,province,1.0,80,0.012607014484682549,0.009824750726394682,-0.000047641501327524856,0.008875866684467599,0.9824999999999999,0.996822033898305,0.703453606965068,0.0,,1.0,2.084646224975586,0.00023630160608743167,0.000031706873333401166,0.00014648997691557965,0.0005048986474166362 | |
| 189 | +heavy_tails,S_sharp,province,1.0,80,0.012609329057167362,0.009825216800870963,-0.000033986049831994565,0.008914833747692542,0.9820833333333333,0.9970338983050848,0.703450769914342,0.0,,1.0,2.0822016537189483,0.00023660925049819595,0.0000324559318594254,0.0001469992472182576,0.0005760546495015698 | |
| 190 | +heavy_tails,S_noEB,province,1.0,80,0.012609329057167362,0.009825216800870963,-0.000033986049831994565,0.008914833747692542,0.9820833333333333,0.9970338983050848,0.703450769914342,0.0,,1.0,1.8373708784580232,0.0002366092504981959,0.000032455931859425385,0.0001469992472182576,0.0005760546495015709 | |
| 191 | +heavy_tails,S_LLT,province,1.0,80,0.012609329057167362,0.009825216800870963,-0.000033986049831994565,0.008914833747692542,0.9820833333333333,0.9970338983050848,0.703450769914342,0.0,,1.0,13.917551755905151,0.0002366092504981959,0.00003245593185942539,0.00014699924721825762,0.0005760546495015706 | |
| 192 | +heavy_tails,K_qhpi,province,1.0,80,0.014072811927853538,0.010972576873583346,-0.0000461376178101108,0.010024965125879196,,,0.6642070993915905,0.0,,1.0,5.130621591210366,0.0002883500105660718,0.0000367635655992913,0.00017631925789305986, | |
| 193 | +heavy_tails,D_rtd13,province,1.0,80,0.014072811927853538,0.010972576873583346,-0.000046137617810110805,0.010024965125879196,,,0.6642070993915905,0.0,,1.0,4.852851694822311,0.00028835001056607177,0.00003676356559929129,0.0001763192578930599, | |
| 194 | +heavy_tails,S_sharp_bottomup,province,1.0,80,0.017503218852440293,0.01373120329839482,-0.000032305821696676165,0.013080836562602796,,,0.5901234555890178,-0.025,,1.0,,0.0003975137171360502,0.000047709822008146657,0.00027047302039256055, | |
| 195 | +heavy_tails,S_coarseloc,province,1.0,80,0.019587854637131967,0.015253565779326803,0.000023842859613542186,0.013901082697011435,0.9810416666666667,0.9949152542372882,0.5410705913151449,0.0125,,1.0,2.0948743492364885,0.000518125754262034,0.00005433456407931688,0.0003435784369801022,0.000645752512977592 | |
| 196 | +heavy_tails,S_norobust,province,1.0,80,0.02248918619638029,0.017465067188440462,-0.00004035360088712019,0.01574629017053581,0.9795833333333333,0.9947033898305084,0.4960749493646922,0.025,,1.0,1.2595425248146057,0.00045611465376767967,0.00005294005351209949,0.0003011502470689318,0.0008882238633953273 | |
| 197 | +heavy_tails,E_fisher_chained,province,1.0,80,0.026497733298031546,0.02048167855250357,-0.00005637100405188549,0.023023639751046966,,,0.428268832797776,0.0125,,1.0,4.078353124856949,0.0005769169018511857,0.00010312315579447206,0.00068490597072037, | |
| 198 | +heavy_tails,B_stratified,province,1.0,80,0.03111079499500654,0.02433720587768887,6.919638238886525e-6,0.022247594807693186,,,0.3686245493791679,-0.0375,,1.0,0.011323928833007812,0.0008273363602892909,0.00008491742966358073,0.0005710262207223511, | |
| 199 | +heavy_tails,A_median,province,1.0,80,0.03585790022907601,0.028221110023796618,-0.00002151304411634047,0.02555563315875457,,,0.313890650743884,-0.0625,,1.0,0.004519528150558472,0.0008754168469439052,0.00010049009140120741,0.0005869442742410799, | |
| 200 | +heavy_tails,H_repeat_sales,province,1.0,80,0.07417194509399774,0.05707482407900159,-0.000162608772409334,0.05221550868037651,,,0.15383556120254702,0.25,,1.0,0.04568012058734894,0.0017107614476155376,0.00019193360623540506,0.0011426625618992606, | |
| 201 | +heavy_tails,K_qhpi,region,4.0,80,0.012112177146000287,0.0097009704805832,-0.00009571958081687598,0.011311694519928316,0.9224479166666667,0.9728813559322035,0.5926076504957432,-0.00625,,1.0,,0.0001427020967381633,0.00002493207642227533,0.000147580145063223,0.00231622076604172 | |
| 202 | +heavy_tails,S_noEB,region,4.0,80,0.021574419733393482,0.017092792675670626,-0.00004785882179777981,0.019314394022676384,0.94375,0.9813559322033898,0.5245672198715343,-0.01875,0.008906977505690155,1.0,,0.000606834757335448,0.000045768882311361626,0.00044609491517714263,0.002603771066998588 | |
| 203 | +heavy_tails,S_coarseloc,region,4.0,80,0.021775063367996398,0.016942850040591657,0.000013235727253616821,0.01854233639061218,0.9851041666666667,0.9966101694915255,0.4850934179343186,0.025,0.00870050446081206,1.0,,0.0005543578719470126,0.00005752272153658312,0.0003977984480401825,0.0010658454405609494 | |
| 204 | +heavy_tails,S_sharp,region,4.0,80,0.023157838129298863,0.018381636145419385,-0.000046706706784276663,0.020383331005431646,0.9357291666666667,0.9780720338983052,0.4966637658618189,-0.025,0.009000100527634248,1.0,,0.0005958068201946369,0.00004669162845984799,0.0004375023370156108,0.0026988585892186263 | |
| 205 | +heavy_tails,S_noTV,region,4.0,80,0.02315817225163116,0.018383204611607994,-0.00006095767335592763,0.020369822445839274,0.9354687499999998,0.9778601694915254,0.49652063486207093,-0.025,0.009002658843783987,1.0,,0.0005920928906910574,0.00004642445598852821,0.0004362450249758042,0.0026888843926406772 | |
| 206 | +heavy_tails,S_LLT,region,4.0,80,0.023362891270743456,0.018519033207720706,-0.00004531760123069039,0.020798809183280358,0.9326041666666667,0.9767478813559322,0.4944494258455716,-0.028125,0.011140861330466916,1.0,,0.0006070140297153415,0.000047822725019867836,0.00044500515093233954,0.00265886645150937 | |
| 207 | +heavy_tails,C_pooled_td,region,4.0,80,0.025575497437327482,0.02002118125068565,-0.000043213545715207596,0.018146108281082787,0.9821875,0.9984639830508474,0.48975324744793786,-0.00625,,1.0,,0.00037132106688549517,0.000036464177113276744,0.00026946576299213826,0.00022205596584883204 | |
| 208 | +heavy_tails,S_norobust,region,4.0,80,0.027217135905554773,0.021146870103763735,-0.0000317780838749989,0.022026460845578494,0.9806250000000001,0.992478813559322,0.4353213420810489,-0.01875,0.00957788272050264,1.0,,0.0007259794058366184,0.00005473448347996121,0.0005075952228251281,0.00118849197963386 | |
| 209 | +heavy_tails,S_sharp_filtered,region,4.0,80,0.029383834552668704,0.023341808338434303,-0.0000638614005894815,0.02370027140329261,0.9133333333333333,0.9481991525423729,0.4225350187912934,0.0125,,1.0,,0.0006585588088189707,0.00005090348129617049,0.000473573220389618,0.0030257625650171767 | |
| 210 | +heavy_tails,D_rtd13,region,4.0,80,0.029873163441812678,0.02333653435397556,-0.00004501237394432333,0.021405425771888392,,,0.43777242070430766,-0.01875,,1.0,,0.00048058892624708896,0.00004077473161834981,0.0003509048001478798, | |
| 211 | +heavy_tails,B_stratified,region,4.0,80,0.06351512188229357,0.048994495332085615,-0.00014359848047580608,0.04575480965156463,,,0.22676976666231372,-0.03125,,1.0,,0.0014132576859336487,0.00009580924611673314,0.001065042475613331, | |
| 212 | +heavy_tails,A_median,region,4.0,80,0.073417094198111,0.05747556068080467,9.551348261792038e-6,0.05202569766861367,,,0.19606677152831664,-0.15625,,1.0,,0.0017157864923159432,0.00010608181554852035,0.0011768633670292666, | |
| 213 | +heavy_tails,H_repeat_sales,region,4.0,80,0.1579607017605112,0.11963814150997454,-0.00031392654648154547,0.11197301590872785,,,0.0835289674985806,0.125,,0.9978645833333333,,0.0030328796218583227,0.0002294131567438619,0.002089613050893106, | |
| 214 | +idiosyncratic_towns,S_LLT,mrc,12.0,80,0.018574100865200904,0.014748087828348197,-0.000027405527525949107,0.020176616079714542,0.9580555555555555,0.9972281073446327,0.5218555134750466,-0.009375,0.022009159125644842,1.0,,0.00021684163954497013,0.00003778458695682598,0.00019560497293282788,0.0015724357873567406 | |
| 215 | +idiosyncratic_towns,S_noTV,mrc,12.0,80,0.018767025451570475,0.014922320318154162,-0.000040951995520664034,0.02006347280151157,0.9699479166666667,0.9986405367231639,0.5282651924814603,-0.008333333333333333,0.014930941166750252,1.0,,0.00021642716631556385,0.000037090971333362754,0.00019182114796963668,0.0013363031505184578 | |
| 216 | +idiosyncratic_towns,S_sharp,mrc,12.0,80,0.018772592253490466,0.014925576708990838,-0.0000250931530518092,0.020079282163081216,0.9698611111111111,0.9986758474576272,0.528143320522354,-0.007291666666666666,0.01492385552477205,1.0,,0.00021647598932873576,0.00003646267080739452,0.00019090859595384795,0.0013524582158070467 | |
| 217 | +idiosyncratic_towns,S_noEB,mrc,12.0,80,0.01880116106047544,0.014949389915902538,-0.000028452137403909483,0.020329290798136872,0.9597048611111111,0.9975459039548022,0.5236354108447411,-0.002083333333333334,0.01395828754308667,1.0,,0.0002159638382805296,0.000036734278300723325,0.00019249965497466362,0.0016292712249107448 | |
| 218 | +idiosyncratic_towns,K_qhpi,mrc,12.0,80,0.020630368557697946,0.01638822921485044,-0.00008098303915290025,0.02181242034838387,0.8722569444444443,0.9637535310734464,0.5103440823912426,-0.002083333333333332,,1.0,,0.000310574707587715,0.000040721218881976446,0.00030038767827492065,0.0036746525221549144 | |
| 219 | +idiosyncratic_towns,S_norobust,mrc,12.0,80,0.0211242790704945,0.016728921013545294,-0.00003689582658640276,0.02116028064423687,0.9803645833333332,0.9989936440677966,0.4825187970405656,-0.01875,0.016373841675285404,1.0,,0.00027994086176860337,0.0000418482765136302,0.0002106205929367766,0.0009820131087770127 | |
| 220 | +idiosyncratic_towns,S_coarseloc,mrc,12.0,80,0.02331653672328319,0.018421294884583987,-0.000036931671836848025,0.023516469985621142,0.9826909722222222,0.9989230225988701,0.41902693791566553,-0.003124999999999998,0.016408986969681724,1.0,,0.0003566178486538558,0.00005239070152997918,0.0002359451070076846,0.0012375690069449435 | |
| 221 | +idiosyncratic_towns,S_sharp_filtered,mrc,12.0,80,0.025519490139897884,0.02005158913878349,0.000010345515519206999,0.026719740764828907,0.9562152777777777,0.9966278248587571,0.44383345591743373,0.15520833333333334,,1.0,,0.0002677957084822341,0.00005872200903603042,0.00029109577901207165,0.0013263833660903733 | |
| 222 | +idiosyncratic_towns,C_pooled_td,mrc,12.0,80,0.05735517678222085,0.043810064583475056,-0.000010156079979171508,0.04095496797051878,0.9774917277916286,0.9931470161572703,0.33335971913185536,-0.012499999999999999,,0.9992361111111112,,0.0009430754054626003,0.00005080020193604043,0.0006577301770501335,0.0003412606646967976 | |
| 223 | +idiosyncratic_towns,D_rtd13,mrc,11.3875,80,0.06116213861448776,0.046828379965060216,-0.000030809627376480264,0.04441306922665977,,,0.3132624776754098,0.022051767676767677,,0.999854797979798,,0.0009287071432290968,0.00005961450939646432,0.0006421401878457334, | |
| 224 | +idiosyncratic_towns,B_stratified,mrc,12.0,80,0.08792124087106591,0.06905872262219044,-0.00008126664215589925,0.06776314330577228,,,0.19793333616609593,0.015625,,0.9970486111111111,,0.0008840140091568622,0.00007203407385463204,0.0007211549740521519, | |
| 225 | +idiosyncratic_towns,A_median,mrc,12.0,80,0.133895074132033,0.10362679523071242,0.00003052493460163961,0.09519908553099762,,,0.16149468211612822,-0.06458333333333334,,0.9992534722222223,,0.002040030618760342,0.00012867577506777797,0.00141675734391746, | |
| 226 | +idiosyncratic_towns,H_repeat_sales,mrc,12.0,80,0.2613026588864792,0.19755692625294008,0.0002543552732175797,0.18553065118159817,,,0.07437255074460666,0.04604166666666666,,0.9336805555555555,,0.003542034639861842,0.0004972649374404735,0.00248050675521635, | |
| 227 | +idiosyncratic_towns,S_LLT,municipality,60.0,80,0.025113466126583595,0.020054589243915625,-0.00009681582397622104,0.04010465500837067,0.9343854166666666,0.9983686440677966,0.3799595378902349,0.025416666666666664,0.05028878688948955,1.0,,0.0001832319302554771,0.00004782283877505297,0.00041499883171322826,0.0012964179374039843 | |
| 228 | +idiosyncratic_towns,S_noTV,municipality,60.0,80,0.025209979479778617,0.020162886504819684,-0.00006269829105043775,0.03753154446853762,0.9647638888888889,0.9994420903954803,0.3892301278421316,0.021458333333333333,0.031046006064705513,1.0,,0.0001738225845478998,0.000042091399681615726,0.0002997114022317972,0.0009975858204364185 | |
| 229 | +idiosyncratic_towns,S_sharp,municipality,60.0,80,0.025211490041317057,0.02016303669298635,-0.00004540432047833879,0.037531908262876404,0.9647881944444444,0.999449152542373,0.38912271738060844,0.020833333333333332,0.031037147406173145,1.0,,0.0001741344790815493,0.00004131093800657875,0.0002976932436937532,0.0009963262205688538 | |
| 230 | +idiosyncratic_towns,S_noEB,municipality,60.0,80,0.025537215031681965,0.020403454957752892,-0.000051406588177525336,0.03931145174635611,0.9312673611111111,0.9983862994350282,0.38227549952877254,0.022708333333333334,0.029858237401627456,1.0,,0.00018175244028122514,0.000040913822053760654,0.00034433591425196214,0.0016892721239600457 | |
| 231 | +idiosyncratic_towns,S_coarseloc,municipality,60.0,80,0.028122388072934518,0.022390980918975487,-0.00005001734743873718,0.03702243183430194,0.9852986111111111,0.9998199152542373,0.3220496495676506,0.03270833333333333,0.03253893259823371,1.0,,0.00029516722058434586,0.00005120104710891143,0.0002539432952275523,0.0006920629876645171 | |
| 232 | +idiosyncratic_towns,S_norobust,municipality,60.0,80,0.028449176296306646,0.02252823395349,-0.00006073376429482478,0.04122360812612455,0.9679270833333333,0.9992902542372881,0.35503026147764427,0.008125000000000002,0.036483419936465554,1.0,,0.0002608540098302618,0.00004933931724765229,0.00042297508826496014,0.0009067644817876964 | |
| 233 | +idiosyncratic_towns,K_qhpi,municipality,60.0,80,0.0329801044899912,0.02561038277230278,-0.00009968071014220065,0.048269811535871884,0.8870486111111111,0.9818608757062147,0.36085355976162276,-0.004999999999999999,,1.0,,0.0003747738538262416,0.00005296765751731789,0.0005313061959242561,0.0020156636234126184 | |
| 234 | +idiosyncratic_towns,S_sharp_filtered,municipality,60.0,80,0.04243087024873947,0.031549431752613974,0.00004504951028013259,0.05538826933020051,0.9542187500000001,0.9976024011299435,0.26760749108794885,0.4845833333333333,,1.0,,0.0003034843441095583,0.00007061393743032263,0.0004723035851067023,0.0007621222659441011 | |
| 235 | +idiosyncratic_towns,B_stratified,municipality,49.425,80,0.06160731419915013,0.04685365145216154,-0.00014661646640405188,0.06754920563132578,,,0.16388510878012888,0.5446626991791821,,0.9678539287252278,,0.0002189851554220027,0.000041986575949985195,0.00023125875869648712, | |
| 236 | +idiosyncratic_towns,D_rtd13,municipality,11.9,80,0.07482606170253493,0.056716732042542226,-0.0000244725095000262,0.05416389658270506,,,0.33027918396423006,0.050499952457673046,,0.9991527791720806,,0.0009564268985779987,0.00006604189348796954,0.0007065913958859989, | |
| 237 | +idiosyncratic_towns,C_pooled_td,municipality,39.375,80,0.12572895316618202,0.09273611594234962,-0.000050982191147886637,0.0899461814363332,0.9678504708546602,0.9824885834500398,0.22847561548444212,-0.002545726037658181,,0.9552058127321746,,0.00109708376861016,0.00011835648111242743,0.0007569726776099995,0.00036845681716058895 | |
| 238 | +idiosyncratic_towns,A_median,municipality,60.0,80,0.2438457396363944,0.19055689814913274,-0.000018701134164426496,0.17358637394582072,,,0.11233120566576345,0.0069877271761702745,,0.81521875,,0.0015314697977814834,0.000577926458275752,0.0010415388974451378, | |
| 239 | +idiosyncratic_towns,H_repeat_sales,municipality,60.0,80,0.391697179097395,0.3056884265696574,-0.0012488431103494975,0.27794098319743066,,,0.06239453408228498,0.03688938438078515,,0.30263541666666666,,0.004211641655086517,0.0008637834976727951,0.0027674593139409348, | |
| 240 | +idiosyncratic_towns,C_pooled_td,province,1.0,80,0.013679493137120708,0.010656606110542286,-0.000012486124878881638,0.010133765300196327,0.9783333333333333,0.9949152542372882,0.692025814078499,0.0,,1.0,0.25402975380420684,0.00029513422073260316,0.00003457120791014377,0.000201808121645267,0.0010440373866378594 | |
| 241 | +idiosyncratic_towns,S_noTV,province,1.0,80,0.013728406909162086,0.010695623701682078,-0.00001408011073368301,0.010164190555974544,0.9791666666666666,0.9951271186440678,0.6907186343460608,0.0,,1.0,2.027424705028534,0.00029668592487889296,0.00003449654347187337,0.00020267776272383427,0.0009138333658465615 | |
| 242 | +idiosyncratic_towns,S_LLT,province,1.0,80,0.01373565532511179,0.01069778843607899,2.8286272437849888e-6,0.010209963129420345,0.9795833333333333,0.9955508474576271,0.6905530408544445,0.0,,1.0,13.75871676504612,0.00029657656925148547,0.00003449610601191079,0.00020331687154200238,0.0008882238633953292 | |
| 243 | +idiosyncratic_towns,S_noEB,province,1.0,80,0.01373565532511179,0.01069778843607899,2.828627243784989e-6,0.010209963129420345,0.9795833333333333,0.9955508474576271,0.6905530408544445,0.0,,1.0,1.788744106888771,0.0002965765692514855,0.00003449610601191079,0.00020331687154200235,0.0008882238633953262 | |
| 244 | +idiosyncratic_towns,S_sharp,province,1.0,80,0.01373565532511179,0.01069778843607899,2.8286272437849888e-6,0.010209963129420347,0.9795833333333333,0.9955508474576271,0.6905530408544445,0.0,,1.0,2.0246143311262133,0.00029657656925148547,0.000034496106011910794,0.0002033168715420024,0.0008882238633953275 | |
| 245 | +idiosyncratic_towns,S_sharp_bottomup,province,1.0,80,0.013968031812331283,0.010925523549263403,-0.00005842368585086105,0.0102079459490909,,,0.686285158827836,0.0,,1.0,,0.0002962210128349629,0.00003305782510912507,0.00019780676783977993, | |
| 246 | +idiosyncratic_towns,D_rtd13,province,1.0,80,0.014409632066708783,0.011228877766151054,-0.00006040201920852108,0.010848084115186934,,,0.6715562581732256,0.0,,1.0,4.830220726132393,0.00030404471348456565,0.000040096509152260436,0.0002246664806284128, | |
| 247 | +idiosyncratic_towns,K_qhpi,province,1.0,80,0.014409632066708783,0.011228877766151054,-0.00006040201920852108,0.010848084115186934,,,0.6715562581732256,0.0,,1.0,4.964584556221962,0.0003040447134845656,0.00004009650915226046,0.00022466648062841269, | |
| 248 | +idiosyncratic_towns,S_norobust,province,1.0,80,0.0168206443836544,0.013126709574165887,-0.000028402708781531348,0.012303087929518257,0.9795833333333333,0.9970338983050848,0.6132388817354459,0.0,,1.0,1.2235188454389572,0.0003604273201486298,0.00004093454018728022,0.00023998577010845835,0.000837279685319463 | |
| 249 | +idiosyncratic_towns,S_coarseloc,province,1.0,80,0.01961533588246307,0.015278810911503637,-0.00003513195914737785,0.014560999210636536,0.9783333333333333,0.9951271186440678,0.5563936900750319,-0.0125,,1.0,2.040228170156479,0.0004615699300754772,0.000054592124591064884,0.00032437811200401387,0.0010440373866378568 | |
| 250 | +idiosyncratic_towns,E_fisher_chained,province,1.0,80,0.022157596063672586,0.01718853842603565,-0.000025696871445501046,0.02010520838403623,,,0.5029051440020552,0.0125,,1.0,3.992170739173889,0.0005066623899522112,0.00008169521843938002,0.0006336611806990805, | |
| 251 | +idiosyncratic_towns,B_stratified,province,1.0,80,0.03154150465363159,0.024691178325035087,-0.00007232728765220344,0.024030049820884983,,,0.40065702041590506,-0.0125,,1.0,0.01047859787940979,0.0008550135553401561,0.00009327272989293443,0.0006601579408781096, | |
| 252 | +idiosyncratic_towns,A_median,province,1.0,80,0.03618936338441822,0.0281659103434475,-0.00003201889354489426,0.026794507354876106,,,0.3465911603517391,-0.0125,,1.0,0.003763526678085327,0.0008252493183308466,0.00011030938757118099,0.0006091549107859456, | |
| 253 | +idiosyncratic_towns,H_repeat_sales,province,1.0,80,0.05664266041441418,0.04337220802351407,0.00007157666563765245,0.04001397621331824,,,0.22111551659715492,0.15,,1.0,0.044797098636627196,0.0012744128331020786,0.00014825021550687856,0.0008290996571758539, | |
| 254 | +idiosyncratic_towns,K_qhpi,region,4.0,80,0.012743401440439278,0.010191070052473824,-0.00007118295902432887,0.012785398803902268,0.9196875,0.9821504237288137,0.586462212536851,0.0125,,1.0,,0.0001346764209595206,0.000029688689003218718,0.00015873955210497277,0.002690907710991174 | |
| 255 | +idiosyncratic_towns,S_LLT,region,4.0,80,0.015721730155398923,0.01239567365089481,-0.000016668229290774356,0.014200078407168118,0.9778645833333334,0.9965572033898304,0.6094538475939103,0.0,0.009053546172092686,1.0,,0.0002537954852460216,0.0000359299703942101,0.00019407793719597698,0.001424975922483935 | |
| 256 | +idiosyncratic_towns,S_noTV,region,4.0,80,0.015775145610647946,0.01245458590936317,-0.00003097319286850607,0.014099385081597166,0.9816145833333334,0.9972457627118644,0.6130958169662482,0.0,0.006506826825395019,1.0,,0.0002534164235574035,0.0000352107965844675,0.00019230589678884865,0.0012172524518593807 | |
| 257 | +idiosyncratic_towns,S_sharp,region,4.0,80,0.015784062726045246,0.012458937571629101,-0.00001464720036100418,0.014130033814851634,0.9823958333333334,0.9972987288135593,0.6129572137550771,0.0,0.006509019817090506,1.0,,0.0002535018937219044,0.000034887196850216506,0.0001919087419810423,0.001133783010659535 | |
| 258 | +idiosyncratic_towns,S_noEB,region,4.0,80,0.01579597280200975,0.012464325047992683,-0.000012614793151942519,0.014188922261671299,0.9792708333333333,0.9967161016949152,0.6102764738990638,0.0,0.006059707097543178,1.0,,0.0002568236679464533,0.00003520739194027669,0.00019184182924460587,0.0012446593893226273 | |
| 259 | +idiosyncratic_towns,S_sharp_filtered,region,4.0,80,0.017646417898049632,0.013994248948117982,-1.8203000105270218e-6,0.016105896191006587,0.9785416666666666,0.9967690677966102,0.5837180339318964,0.015625,,1.0,,0.00026528654277096483,0.00004044973631706418,0.00021039218715369502,0.0012515373808849926 | |
| 260 | +idiosyncratic_towns,S_norobust,region,4.0,80,0.018391815002095118,0.014456433989330303,-0.0000334511385582063,0.015439753069726536,0.9895833333333334,0.9976165254237289,0.552052309600335,-0.003125,0.006700037443553179,1.0,,0.00032053196237210056,0.00004006909195146692,0.00022145787397383462,0.0008580204050435928 | |
| 261 | +idiosyncratic_towns,S_coarseloc,region,4.0,80,0.021103980271182186,0.016565018337590703,-0.000041386213638289635,0.017786584027314523,0.9868229166666668,0.996927966101695,0.4869796241679018,0.0125,0.006887283342858035,1.0,,0.00040263652645984734,0.00005110655917652661,0.00027860467261118556,0.0010598200617101179 | |
| 262 | +idiosyncratic_towns,C_pooled_td,region,4.0,80,0.028906300914925742,0.022452064805124065,0.00001814511324678265,0.020904434461973622,0.9785937499999999,0.9946504237288135,0.4707760566820376,-0.0125,,1.0,,0.0004932954925836098,0.00003801258673522236,0.00034169027196151366,0.0004902337675927505 | |
| 263 | +idiosyncratic_towns,D_rtd13,region,4.0,80,0.031064134749136725,0.02411597815389731,-0.000010542138266576438,0.022673904812074062,,,0.445785633348463,0.003125,,1.0,,0.0005185436955867149,0.00004177786238131114,0.0003617626082556224, | |
| 264 | +idiosyncratic_towns,B_stratified,region,4.0,80,0.06472381936286867,0.05029163101287062,0.0000410600780428757,0.0465812819731453,,,0.24862485262984174,-0.0625,,1.0,,0.0012741937361451137,0.00008641664044742496,0.0009051224003871911, | |
| 265 | +idiosyncratic_towns,A_median,region,4.0,80,0.07494473539125017,0.05854675769452986,-0.00006619264486449194,0.05406380442884007,,,0.2125125625461234,-0.009375,,1.0,,0.0014491297321228829,0.00011141784243310961,0.0010466211562695886, | |
| 266 | +idiosyncratic_towns,H_repeat_sales,region,4.0,80,0.12588704530610498,0.09559943421654618,0.0000862126436666812,0.08915982114227086,,,0.12498850219656495,0.009375,,0.9985937500000001,,0.0022327529075666314,0.00019603639144415177,0.0015410646033642188, | |
| 267 | +no_local_dynamics,S_LLT,mrc,12.0,80,0.012491527826647432,0.009824787200728961,0.000015533501852572576,0.01165390268165124,0.987517361111111,0.9986935028248588,0.6896892492038211,-0.003125,0.017066792690622355,1.0,,0.0002456947203889031,0.000034059094285574025,0.00023797600739250028,0.0008782345355915958 | |
| 268 | +no_local_dynamics,S_sharp,mrc,12.0,80,0.012498879453789531,0.009836060930365775,0.000014491047903347702,0.010784686757183503,0.9908333333333333,0.9988170903954803,0.6891356039887282,0.0,0.008565435340366287,1.0,,0.00024283777337884554,0.00003397530516641682,0.00021143350334489283,0.0007422866106890185 | |
| 269 | +no_local_dynamics,S_noTV,mrc,12.0,80,0.01250887482499668,0.009841322610997084,4.2707776696992994e-6,0.010767927467565608,0.9906597222222222,0.998781779661017,0.688820182096057,0.0,0.008566047571741509,1.0,,0.0002437444282759965,0.00003383832007256796,0.0002105350193913338,0.0007329243141561756 | |
| 270 | +no_local_dynamics,S_noEB,mrc,12.0,80,0.012623235886983683,0.009938681143175783,0.000019105601056979813,0.011081759423935644,0.9876388888888888,0.9984639830508474,0.685986872408083,-0.003125,0.008554025801111684,1.0,,0.00024231457041508906,0.00003414042233842831,0.00019825303654721682,0.0008514498174796908 | |
| 271 | +no_local_dynamics,S_sharp_filtered,mrc,12.0,80,0.01583044653670036,0.01190775085455054,0.000028009508279973095,0.016546066616523702,0.9827256944444445,0.9988170903954803,0.6079752607479474,0.008333333333333333,,1.0,,0.00027058116871858987,0.00004828398528055264,0.0003079851457922859,0.001018358471727786 | |
| 272 | +no_local_dynamics,S_norobust,mrc,12.0,80,0.016220652485519984,0.012726597402071198,4.5438642845748577e-7,0.013122628576119016,0.9894618055555556,0.9986052259887005,0.5931451129094027,0.0020833333333333333,0.009566114737884478,1.0,,0.00032488806495257035,0.000041403300900450345,0.00029037094296796757,0.0008924384088736836 | |
| 273 | +no_local_dynamics,K_qhpi,mrc,12.0,80,0.016984395615298062,0.013428438595593301,-0.00004182053159729357,0.01700512199137063,0.8287847222222222,0.8968220338983051,0.5368954905669578,-0.034375,,1.0,,0.00036461258148649773,0.00003662343011131613,0.0003428712668262568,0.0055739287756671535 | |
| 274 | +no_local_dynamics,S_coarseloc,mrc,12.0,80,0.018828931874048264,0.014637857782285995,0.000080136442874758,0.014337313987526029,0.9908506944444444,0.9973693502824859,0.5429971510180094,0.00625,0.011214867871770288,1.0,,0.0004467654982605673,0.000050184727203300614,0.0003434096589503358,0.0008579114558591205 | |
| 275 | +no_local_dynamics,C_pooled_td,mrc,12.0,80,0.053823533718337334,0.04126839867405712,0.0000359818064150176,0.03817703216661679,0.9783967108274126,0.9938807569451917,0.2453442063371507,-0.017708333333333333,,0.9993055555555556,,0.000850248430952286,0.00005368127738350296,0.000600411260339409,0.000292809243385934 | |
| 276 | +no_local_dynamics,D_rtd13,mrc,11.2875,80,0.05911107908455322,0.0454117200535929,0.000026103700524674063,0.042644939075367615,,,0.22318713821299924,-0.024419191919191917,,0.9998892045454545,,0.000852589322060538,0.00005813239789613793,0.0006033516732807397, | |
| 277 | +no_local_dynamics,B_stratified,mrc,12.0,80,0.08461945300073499,0.06666498237891748,0.00010970982530886322,0.06512067037434321,,,0.14008397593221358,0.10625,,0.996857638888889,,0.0008716688058835739,0.0000738807412061275,0.00071285577451768, | |
| 278 | +no_local_dynamics,A_median,mrc,12.0,80,0.12979452870253277,0.1009365442656516,-0.000028978229634707208,0.09202966232512436,,,0.10970311148214654,0.006249999999999996,,0.9993055555555556,,0.0020051468722828477,0.00012034148987943393,0.0014271608552252487, | |
| 279 | +no_local_dynamics,H_repeat_sales,mrc,12.0,80,0.25114342386304067,0.19073618692615268,-0.00040468642916676324,0.1787235941912232,,,0.047645502014462195,-0.062196969696969695,,0.9325520833333334,,0.0034905370789361837,0.0004365624716692579,0.0025454376809575167, | |
| 280 | +no_local_dynamics,S_sharp,municipality,60.0,80,0.013509941299925516,0.010615785792157757,0.000017392525537842403,0.017621636719784804,0.9910902777777778,0.9998093220338984,0.6731279944948477,0.0020833333333333333,0.02203534699316561,1.0,,0.0002385619191488271,0.0000384783866007532,0.00036240321922829385,0.0004674245141976259 | |
| 281 | +no_local_dynamics,S_noTV,municipality,60.0,80,0.0135137653229018,0.010618129185190773,8.489950062947692e-6,0.017618009618218352,0.9909965277777777,0.9998093220338984,0.6729424075663716,0.003333333333333333,0.02204551640579127,1.0,,0.0002419437340827899,0.00003840318787614015,0.00036484949451617203,0.00047147946026956873 | |
| 282 | +no_local_dynamics,S_LLT,municipality,60.0,80,0.014012662081944474,0.011004226487943292,0.000021494841008363798,0.025726566100063002,0.9755173611111111,0.999774011299435,0.6672918831036808,0.004791666666666667,0.04522876412063302,1.0,,0.0002486716801459239,0.00004274059200764061,0.0004883829111837372,0.000884059486307412 | |
| 283 | +no_local_dynamics,S_noEB,municipality,60.0,80,0.01401583315803798,0.01101011428006912,0.00001846428496753383,0.018968506096718223,0.988451388888889,0.9996115819209039,0.6644877235704911,0.0006249999999999999,0.022451873266192794,1.0,,0.00023866607081585403,0.00003985938947469643,0.00036688901133152964,0.0005483840705938458 | |
| 284 | +no_local_dynamics,S_coarseloc,municipality,60.0,80,0.018847042322808823,0.014653044198158518,0.00008322108933776182,0.014973447093082261,0.9967916666666667,0.9992690677966103,0.5425538412447769,0.006041666666666667,0.025243807133891943,1.0,,0.0004445625895305482,0.000049603144398062964,0.00030300669654925125,0.00039933784954716427 | |
| 285 | +no_local_dynamics,S_norobust,municipality,60.0,80,0.021304122275261194,0.01631111300091915,0.00002366041416043156,0.02701192715566771,0.9819027777777778,0.9978601694915253,0.5435437626499137,-0.0008333333333333333,0.02887653212442858,1.0,,0.00044414793517159445,0.000048936210565353924,0.0007221296445828104,0.0007968009050535216 | |
| 286 | +no_local_dynamics,S_sharp_filtered,municipality,60.0,80,0.027317609061019942,0.01748729993272263,0.000012861187724500515,0.036974540043276966,0.9757222222222222,0.9989194915254236,0.4514176164246629,0.05083333333333333,,1.0,,0.00036642601077585947,0.00006393309218712737,0.0005639215226918527,0.0006602258607603548 | |
| 287 | +no_local_dynamics,K_qhpi,municipality,60.0,80,0.028442825773907426,0.021372653744360048,-6.167940324801322e-6,0.03907789668587042,0.9034652777777777,0.9564442090395481,0.5051308978269564,-0.0012500000000000015,,1.0,,0.00048103140412593794,0.000048881198112948615,0.0006649385894860246,0.0017939280953295418 | |
| 288 | +no_local_dynamics,B_stratified,municipality,49.1875,80,0.059736410813210315,0.04466272065981617,-0.00010232336888670157,0.06487608643039415,,,0.08660340219886542,0.3393210405546086,,0.9679102773679003,,0.00019027839772641565,0.000037174828644471827,0.00022174272130222642, | |
| 289 | +no_local_dynamics,D_rtd13,municipality,11.8375,80,0.07476282668973086,0.056715721381480344,0.000018145587446902127,0.05398720416780095,,,0.17633082020039986,0.02661866217932394,,0.9994519916188114,,0.0009692170536445496,0.000059285881559408316,0.0006597113594865191, | |
| 290 | +no_local_dynamics,C_pooled_td,municipality,39.2625,80,0.12521862658479097,0.09244265122338605,-0.00017865483901299695,0.08980781445638944,0.9677307837932808,0.9830416220664222,0.12406557164210426,0.024942946053427242,,0.9565275781612005,,0.0010064061018635374,0.00011919049163519523,0.0006854945865346332,0.0003902054629756152 | |
| 291 | +no_local_dynamics,A_median,municipality,60.0,80,0.24275115114555348,0.19000019532092757,-0.0004303374215955679,0.17293378571644794,,,0.0626415008769059,-0.012922893026870514,,0.815420138888889,,0.0016306233554351922,0.0006524609448272027,0.001163957125309774, | |
| 292 | +no_local_dynamics,H_repeat_sales,municipality,60.0,80,0.3924757575720383,0.30516575779939276,0.00006554050366930913,0.27634672981076797,,,0.02114101015763045,-0.041851447616056886,,0.3024444444444444,,0.004539086133894917,0.0008666325745813936,0.002930413811454439, | |
| 293 | +no_local_dynamics,C_pooled_td,province,1.0,80,0.012188709072161857,0.009573205003020235,-6.279651389545404e-7,0.008588733147127844,0.9808333333333333,0.9972457627118644,0.6992133710894815,0.0,,1.0,0.2637650310993195,0.00023983173383832155,0.000031728067264677374,0.00016228542982186915,0.0007322681177978285 | |
| 294 | +no_local_dynamics,S_sharp,province,1.0,80,0.012241899209203533,0.009620706428763835,9.489578494838662e-6,0.008652802697372845,0.9808333333333333,0.9970338983050848,0.6976625080536836,0.0,,1.0,2.0883998066186904,0.00024096260834707739,0.00003198046659773446,0.0001637193702020422,0.0007322681177978279 | |
| 295 | +no_local_dynamics,S_LLT,province,1.0,80,0.012241899209203533,0.009620706428763835,9.48957849483866e-6,0.008652802697372845,0.9808333333333333,0.9970338983050848,0.6976625080536836,0.0,,1.0,14.12926687002182,0.00024096260834707739,0.00003198046659773446,0.00016371937020204228,0.0007322681177978291 | |
| 296 | +no_local_dynamics,S_noEB,province,1.0,80,0.012241899209203533,0.009620706428763835,9.489578494838662e-6,0.008652802697372845,0.9808333333333333,0.9970338983050848,0.6976625080536836,0.0,,1.0,1.8283181637525558,0.00024096260834707733,0.00003198046659773446,0.0001637193702020423,0.0007322681177978265 | |
| 297 | +no_local_dynamics,S_noTV,province,1.0,80,0.012246173378082377,0.009620253387006889,-1.5508413340052671e-6,0.008627447307313952,0.9810416666666667,0.9972457627118644,0.6974333583761717,0.0,,1.0,2.0950048178434373,0.00024093282598626237,0.00003187595519004596,0.00016336268067107084,0.0007105636270406287 | |
| 298 | +no_local_dynamics,S_sharp_bottomup,province,1.0,80,0.012297048988834715,0.009662645590540506,0.000010354636700117162,0.008873871137812376,,,0.6955412578564449,0.0,,1.0,,0.00024129116235659936,0.000033348901292024574,0.00016371126424165138, | |
| 299 | +no_local_dynamics,K_qhpi,province,1.0,80,0.013856891898876992,0.010802171563172916,-0.000026174987898760365,0.009937309073723196,,,0.6551946559883992,0.0,,1.0,5.231458052992821,0.0002766618269150113,0.000039418965682207716,0.00019546372687958668, | |
| 300 | +no_local_dynamics,D_rtd13,province,1.0,80,0.013856891898876992,0.010802171563172916,-0.000026174987898760365,0.009937309073723196,,,0.6551946559883992,0.0,,1.0,4.988562515377998,0.00027666182691501117,0.00003941896568220772,0.0001954637268795867, | |
| 301 | +no_local_dynamics,S_norobust,province,1.0,80,0.015719383687836333,0.012303937397463546,-9.449550586012311e-6,0.011031822620247331,0.98125,0.9978813559322035,0.6037355888644851,0.0,,1.0,1.2317226111888886,0.0003002119724745353,0.000040298626179479165,0.0002028371135548994,0.0006873761236051087 | |
| 302 | +no_local_dynamics,S_coarseloc,province,1.0,80,0.018660976777847192,0.014495760921708043,0.00007887770460110654,0.01325554325656512,0.9799999999999999,0.9955508474576271,0.5468888024415743,0.0,,1.0,2.0957900524139403,0.00044789500199649305,0.000049077869358012483,0.0003253321841075006,0.0008065310064816553 | |
| 303 | +no_local_dynamics,E_fisher_chained,province,1.0,80,0.021155710239067756,0.016428143443258207,4.691837383640336e-6,0.01790580794641785,,,0.49434445288577694,-0.0125,,1.0,4.133280393481255,0.0004755922115401298,0.00007075114864178715,0.0005138607808828573, | |
| 304 | +no_local_dynamics,B_stratified,province,1.0,80,0.0306576503863072,0.023838026584879955,0.00021589066575477045,0.021792058816744255,,,0.3730631956007557,0.0125,,1.0,0.013623049855232239,0.0007166716810004021,0.00008020726813367818,0.0005161943622937385, | |
| 305 | +no_local_dynamics,A_median,province,1.0,80,0.035640028517905174,0.028057062251136734,0.00008015813905134036,0.025306484629257796,,,0.3344299546690607,0.0375,,1.0,0.004598623514175415,0.0007721066379372289,0.00010371628960011762,0.0005384788628060971, | |
| 306 | +no_local_dynamics,H_repeat_sales,province,1.0,80,0.05281208435935453,0.04062598516122347,-0.0000803810835633375,0.03700316995748894,,,0.20473549506739075,-0.025,,1.0,0.046198314428329466,0.0009811599284147152,0.00016193555255133372,0.0006956847199850191, | |
| 307 | +no_local_dynamics,K_qhpi,region,4.0,80,0.010767631486340697,0.008631292865317877,-0.00008057233550179976,0.010526210854691732,0.9370833333333334,0.9836334745762713,0.5538828948051645,-0.01875,,1.0,,0.00012061208156594577,0.000023823202264919435,0.00012088216650801176,0.0028074409492533045 | |
| 308 | +no_local_dynamics,S_LLT,region,4.0,80,0.012329872115116155,0.009690045114640757,8.396549854264388e-6,0.009530522959124273,0.9886458333333333,0.9976165254237289,0.694039629010271,0.0,0.006358779235829286,1.0,,0.00024031964103700087,0.000033060625974466643,0.00019038456777087997,0.000910746972253192 | |
| 309 | +no_local_dynamics,S_sharp,region,4.0,80,0.01234218870929289,0.009701794335551085,3.2934988876856323e-6,0.009398339434466942,0.98890625,0.9977224576271186,0.6937086396382586,0.0,0.003318124649700784,1.0,,0.0002396384003402652,0.00003279227600496339,0.00017413472814177587,0.0009490577705339295 | |
| 310 | +no_local_dynamics,S_noTV,region,4.0,80,0.012346500034626022,0.009702576797628296,-7.629666398417149e-6,0.00937645122477301,0.9891666666666667,0.9975635593220339,0.6934889348936125,0.0,0.0033157938232102483,1.0,,0.0002395726962841065,0.00003267088132981328,0.0001737896452071404,0.000909615187334479 | |
| 311 | +no_local_dynamics,S_noEB,region,4.0,80,0.012351114573545508,0.009710841113350835,1.5292907367409876e-6,0.00939242589056033,0.9871354166666666,0.9975635593220339,0.6935337195378211,0.0,0.003167318819853567,1.0,,0.0002396880002623294,0.00003288913268569565,0.00017228653000335423,0.0008424923559378119 | |
| 312 | +no_local_dynamics,S_sharp_filtered,region,4.0,80,0.0130058731434412,0.010231528693503748,0.0000159590844169841,0.010696761764094507,0.9920312499999999,0.9981991525423728,0.6754267045350073,0.0,,1.0,,0.00024049823619923803,0.00003591451282774029,0.00020289463248099017,0.0009083874957238709 | |
| 313 | +no_local_dynamics,S_norobust,region,4.0,80,0.01577138507219587,0.012350800395898883,-0.000012711590043669042,0.011478958932464476,0.9878125000000001,0.9979872881355932,0.6013361122567983,0.0,0.0032844059312665923,1.0,,0.00029916728509842314,0.000040288050682918,0.0002069210117372431,0.0010129886737245573 | |
| 314 | +no_local_dynamics,S_coarseloc,region,4.0,80,0.018693845931698996,0.014525515195315702,0.00007762625153953338,0.013657604938889813,0.9868750000000001,0.9961864406779661,0.5450936518571876,0.00625,0.004039961898800846,1.0,,0.00044718522010933853,0.0000492362813617256,0.0003202854620988178,0.0010219655952714006 | |
| 315 | +no_local_dynamics,C_pooled_td,region,4.0,80,0.026132521912302382,0.02033373511380337,0.00001529098217662004,0.018505442840452045,0.9799999999999999,0.9951271186440678,0.4283559961411778,0.003125,,1.0,,0.00039489982057469325,0.00003504090108581417,0.0002747211430100685,0.0003879899697110017 | |
| 316 | +no_local_dynamics,D_rtd13,region,4.0,80,0.029851949911929215,0.023191259106358213,-3.0298440781763084e-6,0.02140433246840032,,,0.386234600399114,-0.021875,,1.0,,0.0004724865116156982,0.000042990585576906714,0.00033257378787812387, | |
| 317 | +no_local_dynamics,B_stratified,region,4.0,80,0.06009599202598727,0.0469461154966013,0.00007953620299358533,0.04295995019125828,,,0.20635292860870263,-0.03125,,1.0,,0.0013154319230996362,0.00008391575506334167,0.000925627141263212, | |
| 318 | +no_local_dynamics,A_median,region,4.0,80,0.07200715770105916,0.05618911670185426,0.00008693061725814173,0.051126413326540665,,,0.1862634862899191,0.071875,,1.0,,0.0015480457233728376,0.00009584734551278744,0.0010644852305792703, | |
| 319 | +no_local_dynamics,H_repeat_sales,region,4.0,80,0.11997248794734106,0.09096983099319951,0.000054668993017720894,0.08448056897802433,,,0.09258066090065006,-0.015625,,0.9985416666666668,,0.0020574371304962844,0.00019069908170066857,0.0014214033567547322, | |
| 320 | +rising_quality_down,S_LLT,mrc,12.0,80,0.014489577669336682,0.011485828367753467,-0.0004354504559028811,0.01701881030167047,0.9505729166666667,0.9987464689265536,0.6195085986151181,0.0010416666666666667,0.0183107295588111,1.0,,0.00021694679481613715,0.00003724530981550508,0.00015917341727081675,0.0022020907293736603 | |
| 321 | +rising_quality_down,S_sharp,mrc,12.0,80,0.014588657203482893,0.01157509661976432,-0.000435401477254076,0.016753885163554624,0.9623611111111112,0.9994703389830508,0.6209645525231496,0.0020833333333333333,0.011072432207129383,1.0,,0.00021617579800905164,0.00003619721307732596,0.00015719104127081944,0.0018346008489704129 | |
| 322 | +rising_quality_down,S_noTV,mrc,12.0,80,0.014591745929972682,0.011574979286474964,-0.0004233273290784755,0.016647232233811128,0.9629340277777778,0.9993820621468927,0.6209302724404485,0.0010416666666666667,0.01107375135123186,1.0,,0.0002154941343086409,0.00003579906109432694,0.00015318348460900997,0.0017433207806547748 | |
| 323 | +rising_quality_down,S_noEB,mrc,12.0,80,0.01460155576788598,0.01158209412554604,-0.0004336819741138246,0.01699497752716705,0.9518229166666666,0.9990466101694915,0.6187228722251729,-0.003125,0.010519951765858356,1.0,,0.00021499675507719758,0.00003685921951686454,0.00016244729051623306,0.0020331499493033353 | |
| 324 | +rising_quality_down,S_norobust,mrc,12.0,80,0.01768076027549094,0.013944313722813476,-0.0004370855967707839,0.018240556210091068,0.9782638888888888,0.9990289548022598,0.5463583812345163,0.0010416666666666673,0.012248084969557321,1.0,,0.00029133342273113846,0.00004155165565220918,0.0001959654269654474,0.0013319233710635803 | |
| 325 | +rising_quality_down,K_qhpi,mrc,12.0,80,0.01869326096638229,0.014851462492072831,-0.00048622284995280705,0.02112301081958586,0.8388541666666667,0.9604343220338982,0.5176095111777791,-0.00625,,1.0,,0.0003406360443783375,0.00003667991205455005,0.0003017830430147897,0.004324256647450067 | |
| 326 | +rising_quality_down,S_sharp_filtered,mrc,12.0,80,0.019345057814750464,0.015042748192113736,-0.0004289216903075049,0.021849450551630453,0.9549479166666666,0.9986228813559321,0.53142737782223,0.04375,,1.0,,0.00025901383771834407,0.000047065439831122956,0.00025639522781979675,0.0015137775426316627 | |
| 327 | +rising_quality_down,S_coarseloc,mrc,12.0,80,0.020143050146987602,0.015762092863819615,-0.00021794726751991675,0.019289545838824286,0.9828645833333333,0.9979872881355932,0.4949621768424719,0.011458333333333333,0.013096732556837881,1.0,,0.0004171960307354339,0.00005621872478343013,0.0002678268304618198,0.001505710999985395 | |
| 328 | +rising_quality_down,C_pooled_td,mrc,12.0,80,0.05406738826830111,0.04147031904593581,-0.00038562608644793683,0.039254952919236745,0.9773362016222695,0.9937231802459632,0.28439855596241975,0.01979166666666667,,0.9993055555555556,,0.000860762820153981,0.00005357873829136458,0.0005906248189229683,0.0003274813925701349 | |
| 329 | +rising_quality_down,D_rtd13,mrc,11.2875,80,0.059226421466853606,0.04548937620981977,-0.0003931174218045833,0.04349849893151099,,,0.2619597859064703,-0.030195707070707067,,0.9998892045454545,,0.0008559944634062181,0.000058155180848132085,0.0005930494875996455, | |
| 330 | +rising_quality_down,B_stratified,mrc,12.0,80,0.08471241612003863,0.06678809305719871,-0.001009304622652013,0.06700368975679341,,,0.1631289269612038,0.08645833333333333,,0.9966840277777778,,0.0008628829008886719,0.00007472308664399967,0.0006973250767788762, | |
| 331 | +rising_quality_down,A_median,mrc,12.0,80,0.1287398036716392,0.10008408755062573,-0.0023911624269888953,0.10151410502530553,,,0.12927628182061845,0.05,,0.9993055555555556,,0.001995814332347882,0.00012086641296046936,0.0012783737864252422, | |
| 332 | +rising_quality_down,H_repeat_sales,mrc,12.0,80,0.2530609447324783,0.19217793842153222,-0.0008303830041114489,0.180304873295074,,,0.06227634976851999,-0.035625000000000004,,0.9325520833333334,,0.003567662843602165,0.00044852811475576474,0.0025887341447120094, | |
| 333 | +rising_quality_down,S_sharp,municipality,60.0,80,0.01575623385561421,0.012494142313964719,-0.00043038391165470355,0.023637575954098534,0.9756770833333333,0.9998834745762712,0.5939059536515239,0.005416666666666666,0.02366771949199075,1.0,,0.00021723223471457602,0.00003993748340588142,0.0003274592929381338,0.0009336695560041345 | |
| 334 | +rising_quality_down,S_noTV,municipality,60.0,80,0.015757006311514065,0.012493269292044822,-0.0004187265789890363,0.023575668688242245,0.9758923611111111,0.9998728813559321,0.5937997133264713,0.005625,0.023685531586437776,1.0,,0.00021653780296994972,0.00003972774324502573,0.00032912457907582497,0.0009319137199885877 | |
| 335 | +rising_quality_down,S_LLT,municipality,60.0,80,0.0160754463446934,0.012724841181746932,-0.0004238926539820142,0.029362846016886174,0.9587083333333334,0.999774011299435,0.5885000524381112,0.008541666666666666,0.04573487836585109,1.0,,0.00021932736418556626,0.000046047354579355645,0.00042868859063680654,0.001098381057650005 | |
| 336 | +rising_quality_down,S_noEB,municipality,60.0,80,0.016158882489072247,0.012798666338701872,-0.00043115953633413235,0.025164489547913633,0.9642048611111111,0.9997245762711865,0.586824228558563,0.008541666666666666,0.02375971853325148,1.0,,0.00021439916299416904,0.00004174143956366195,0.0003411858387224164,0.0011603954557977841 | |
| 337 | +rising_quality_down,S_coarseloc,municipality,60.0,80,0.02045238004225839,0.016025599909925442,-0.00022019792950699952,0.02163885926286382,0.9922986111111112,0.999579802259887,0.4818421830505608,0.014166666666666666,0.02672395379737994,1.0,,0.00040925967440340105,0.00005397834101625137,0.0002403711428351068,0.00047748205674148623 | |
| 338 | +rising_quality_down,S_norobust,municipality,60.0,80,0.022335568036755644,0.017245847956215365,-0.000419034682036663,0.03127925190670451,0.9744131944444444,0.9982944915254237,0.4972210521012131,-0.0002083333333333333,0.03024111396332807,1.0,,0.00038075498950828924,0.000049660030490153814,0.0005569134643538416,0.0008511772432322131 | |
| 339 | +rising_quality_down,K_qhpi,municipality,60.0,80,0.029070741682206767,0.02207228166235654,-0.0004474850772020596,0.04344829931062598,0.8703888888888889,0.9756956214689266,0.4591518847341243,0.016458333333333332,,1.0,,0.00047058242317442995,0.00004833041207210385,0.0006272304273442088,0.0024397510402298928 | |
| 340 | +rising_quality_down,S_sharp_filtered,municipality,60.0,80,0.030155433835608165,0.020583335542497957,-0.00044939985199535075,0.04059654234578965,0.9624027777777778,0.9989830508474575,0.40158082466328776,0.11583333333333332,,1.0,,0.00036646392138445325,0.00006046932336982754,0.0005222642175504031,0.0008636806067630056 | |
| 341 | +rising_quality_down,B_stratified,municipality,49.1875,80,0.0600400577820953,0.04509456331034821,-0.001533270048822232,0.06821583876507895,,,0.10488811795676829,0.40828680090793795,,0.9672253979546742,,0.00020011100771691619,0.00003750496020437454,0.0002768884343036105, | |
| 342 | +rising_quality_down,D_rtd13,municipality,11.8375,80,0.07480124656704369,0.056734253576038694,-0.0004226194757268679,0.05465619384475969,,,0.2144849388544258,-0.012985477553492265,,0.9994519916188114,,0.0009688887914619922,0.00006074216805393253,0.0006419916859749636, | |
| 343 | +rising_quality_down,C_pooled_td,municipality,39.2625,80,0.125351292528187,0.09254900551238038,-0.0006353740259328752,0.09035744682878627,0.9674297402197036,0.9830816193429237,0.14930770312784614,0.010050581714458986,,0.9565275781612005,,0.00100961897323666,0.00011924910437701771,0.0006813881259019556,0.0003701369356313452 | |
| 344 | +rising_quality_down,A_median,municipality,60.0,80,0.23951825534662427,0.18738173747148623,-0.0028177397472055016,0.17635516793705644,,,0.07439876051016481,-0.0007856556835300295,,0.815420138888889,,0.0016365596056434827,0.0006351866670417568,0.0011244390062864398, | |
| 345 | +rising_quality_down,H_repeat_sales,municipality,60.0,80,0.3980406845482537,0.31001203306558645,-0.000826878758620526,0.28115533850053853,,,0.032942725820679766,-0.10664272273441264,,0.3024444444444444,,0.0046621865377517585,0.0008625293571059762,0.0030221685352721573, | |
| 346 | +rising_quality_down,C_pooled_td,province,1.0,80,0.012411808576805608,0.009746223520656074,-0.0004250315242982167,0.011557401747355863,0.968125,0.9972457627118644,0.7072875320771057,0.0,,1.0,0.2744682222604752,0.0002496993373503071,0.00003157973532580467,0.0001459303865803946,0.002141975301605123 | |
| 347 | +rising_quality_down,S_sharp,province,1.0,80,0.01246417039598373,0.009792130492421882,-0.00043812675177694056,0.011723784821024584,0.9685416666666666,0.9972457627118644,0.7056783875474532,0.0,,1.0,2.1377365082502364,0.00025107032754433534,0.000031745266903899605,0.00015530802411343697,0.0021986792400993543 | |
| 348 | +rising_quality_down,S_noEB,province,1.0,80,0.01246417039598373,0.009792130492421882,-0.00043812675177694067,0.011723784821024584,0.9685416666666666,0.9972457627118644,0.7056783875474532,0.0,,1.0,1.8720806211233139,0.0002510703275443353,0.000031745266903899605,0.00015530802411343702,0.0021986792400993547 | |
| 349 | +rising_quality_down,S_LLT,province,1.0,80,0.01246417039598373,0.009792130492421882,-0.00043812675177694067,0.011723784821024584,0.9685416666666666,0.9972457627118644,0.7056783875474532,0.0,,1.0,13.944825065135955,0.00025107032754433524,0.00003174526690389961,0.00015530802411343697,0.0021986792400993512 | |
| 350 | +rising_quality_down,S_noTV,province,1.0,80,0.01246591873492677,0.009788789657467182,-0.00042552770914941024,0.011588116040074646,0.9679166666666668,0.9972457627118644,0.7056397289389642,0.0,,1.0,2.1566160291433336,0.00025066555385996344,0.00003168316070405694,0.00014602984082656784,0.0020917551295025617 | |
| 351 | +rising_quality_down,S_sharp_bottomup,province,1.0,80,0.012631577405157343,0.00993849671094511,-0.0004478840856000225,0.012124926229511047,,,0.7010531578450172,0.0,,1.0,,0.0002501152855372511,0.00003449716606982408,0.00015229647627231367, | |
| 352 | +rising_quality_down,D_rtd13,province,1.0,80,0.013901406002923018,0.010844434188839855,-0.00045419222516812836,0.01268391783177886,,,0.6702082271552571,0.0,,1.0,4.947356307506562,0.00028030927776832024,0.000038514734799423514,0.00021840224019882874, | |
| 353 | +rising_quality_down,K_qhpi,province,1.0,80,0.013901406002923018,0.010844434188839855,-0.00045419222516812836,0.01268391783177886,,,0.6702082271552571,0.0,,1.0,5.289524349570274,0.0002803092777683203,0.00003851473479942352,0.0002184022401988288, | |
| 354 | +rising_quality_down,S_norobust,province,1.0,80,0.015866473708097142,0.012418602814412028,-0.0004610448928794553,0.01377339466311637,0.9766666666666666,0.9976694915254237,0.6155365536876433,0.0,,1.0,1.3240382820367813,0.0003080192680771947,0.000039701136628701664,0.0001908811280831184,0.0015576136676187491 | |
| 355 | +rising_quality_down,S_coarseloc,province,1.0,80,0.018752784268279928,0.014572612476415222,-0.00022886926428198638,0.014520625121561822,0.9777083333333334,0.9955508474576271,0.5619124749830708,0.0,,1.0,2.134684318304062,0.00044580196423694426,0.000053815957387732906,0.00028878818066737467,0.0011838602707443867 | |
| 356 | +rising_quality_down,E_fisher_chained,province,1.0,80,0.021135284349298785,0.01641327045752882,-0.0002785544245805017,0.01878628525566297,,,0.5114161063735255,0.0125,,1.0,4.185856792330742,0.000468508433711882,0.00007157930691157664,0.0005147855894714281, | |
| 357 | +rising_quality_down,B_stratified,province,1.0,80,0.030608624138563523,0.023861729820355247,-0.0003300355834197847,0.02456109234830584,,,0.37930996728394406,0.0125,,1.0,0.012602460384368897,0.0007178810688954402,0.00009112855572816664,0.00042850032433997454, | |
| 358 | +rising_quality_down,A_median,province,1.0,80,0.03549127985429378,0.027895057107575623,-0.002266500747605665,0.04812111776890271,,,0.34722128217316345,0.1,,1.0,0.004868859052658081,0.0007963811252646218,0.0001036384244204621,0.0004737732701299174, | |
| 359 | +rising_quality_down,H_repeat_sales,province,1.0,80,0.05332453518808915,0.04103243720976506,-0.00039097227028113673,0.03789970310833561,,,0.21576362164774116,-0.0125,,1.0,0.04627225697040558,0.0010047534729827447,0.00016502561054323965,0.0007057619176553965, | |
| 360 | +rising_quality_down,K_qhpi,region,4.0,80,0.012468835308152064,0.009980883240917473,-0.0005465594935367393,0.013994712647107485,0.8765625,0.98125,0.5789083521705612,-0.025,,1.0,,0.0001292018340600769,0.000023989286454307123,0.00013043967925878045,0.0036503513295935677 | |
| 361 | +rising_quality_down,S_LLT,region,4.0,80,0.013899190529472782,0.011011425196130322,-0.0004418090505420226,0.014469206203002035,0.9613020833333333,0.9985169491525424,0.6430626714140149,0.003125,0.008188005392366405,1.0,,0.00022081582358851444,0.00003641858644466384,0.0001648358758651513,0.0023905747991888617 | |
| 362 | +rising_quality_down,S_noEB,region,4.0,80,0.013923792020292858,0.011033337606707062,-0.00044292961877689897,0.014434769717125923,0.9613541666666666,0.9985699152542373,0.6430499670603903,0.003125,0.005262667761868142,1.0,,0.00022035468486217316,0.00003521661054682141,0.00016079495847106515,0.0025611961109285875 | |
| 363 | +rising_quality_down,S_sharp,region,4.0,80,0.013980680308357408,0.01107718787887596,-0.00044504397056719503,0.014435787992859059,0.9663020833333332,0.9990466101694915,0.6441487920156971,0.003125,0.005863379475378544,1.0,,0.00022180148305679206,0.00003503718464760361,0.00016012819780717824,0.0023158649421526852 | |
| 364 | +rising_quality_down,S_noTV,region,4.0,80,0.013980807971929552,0.011074366418979657,-0.00043278194044352555,0.01431945916680392,0.9674479166666666,0.9989936440677966,0.6441516998188936,0.003125,0.005862398459970152,1.0,,0.00022125711066400683,0.00003488817733884109,0.00015651001954164915,0.0022507784904009604 | |
| 365 | +rising_quality_down,S_sharp_filtered,region,4.0,80,0.015777657301355906,0.012547472090871082,-0.0004347852087878035,0.01604800017076604,0.9651041666666667,0.998040254237288,0.6083803402517578,0.00625,,1.0,,0.00023947625727865842,0.00003690020459528292,0.00019341212457471725,0.002153837331695797 | |
| 366 | +rising_quality_down,S_norobust,region,4.0,80,0.01696589186649205,0.013356937723810237,-0.00045118326570191774,0.01574325240938438,0.98140625,0.998728813559322,0.5674911953466258,0.00625,0.005967499671955913,1.0,,0.00028385819583550466,0.000040745398072323535,0.0001915190661382111,0.0015634995923564797 | |
| 367 | +rising_quality_down,S_coarseloc,region,4.0,80,0.019694361156803196,0.015389537501005718,-0.00022763250371616732,0.01690359453738096,0.9838541666666666,0.9964512711864406,0.5131138115426037,0.003125,0.006076317745248668,1.0,,0.0004197575733563607,0.00005501405531875186,0.0002820129058678256,0.0015886524439136288 | |
| 368 | +rising_quality_down,C_pooled_td,region,4.0,80,0.026316314435272638,0.02050498479021368,-0.00039728182747198837,0.02015757371679581,0.9778125,0.9952330508474576,0.4729600626192334,-0.015625,,1.0,,0.0004017679527616205,0.000035593129239321206,0.00025636992901709795,0.000587271029983794 | |
| 369 | +rising_quality_down,D_rtd13,region,4.0,80,0.029917353495569933,0.0232503895334277,-0.00042427814574813995,0.02284568105047179,,,0.4324688138786537,-0.034375,,1.0,,0.0004822192967400495,0.000043082268310006244,0.00032441189728102366, | |
| 370 | +rising_quality_down,B_stratified,region,4.0,80,0.06089584491033503,0.04759472363472133,-0.0005783494280832888,0.045147154899399966,,,0.2344710411557168,0.003125,,0.9999479166666667,,0.0013520696816905476,0.0000821743045922866,0.0008757384664698958, | |
| 371 | +rising_quality_down,A_median,region,4.0,80,0.0719442132976279,0.05612614016563837,-0.0022843829108894253,0.06606477189199904,,,0.21047963325786817,0.065625,,1.0,,0.001554062830548758,0.00009330711786024021,0.0008257610265034544, | |
| 372 | +rising_quality_down,H_repeat_sales,region,4.0,80,0.12041710047093537,0.09129182076700978,-0.0002643146512552566,0.08513135106452863,,,0.11461152766977259,-0.046875,,0.9985416666666668,,0.0020461736101459876,0.00019420049060035134,0.001408787145957303, | |
| 373 | +sparse,S_LLT,mrc,12.0,80,0.02282571982788023,0.017875595490957602,0.00002518088658477786,0.022368921108099328,0.9825347222222222,0.9986758474576272,0.43911558808959306,0.020833333333333336,0.029265091322037185,1.0,,0.0004109384840648807,0.00006081264751057994,0.0003481729220724966,0.001134321298905149 | |
| 374 | +sparse,S_noTV,mrc,12.0,80,0.02288113027658248,0.017934172246963583,5.1474740365974315e-6,0.021559422289537278,0.9870833333333333,0.9989406779661018,0.4421183414157833,0.015625,0.016404486175372084,1.0,,0.0004099578062379961,0.000059277203609093285,0.0003177031423092534,0.0009982452568126033 | |
| 375 | +sparse,S_sharp,mrc,12.0,80,0.022894119702417535,0.017946217269606428,0.000019383661768945693,0.02162351708771463,0.9869791666666666,0.9989936440677966,0.4416514874678093,0.016666666666666663,0.01639657598596945,1.0,,0.0004103321314794901,0.000059290828880267445,0.0003158168677691259,0.000993640592670568 | |
| 376 | +sparse,S_noEB,mrc,12.0,80,0.022990557514750383,0.01802510313187792,0.00002634509301874911,0.022107941105967078,0.9831423611111111,0.9986052259887005,0.43862200161701487,0.016666666666666666,0.01589363782939702,1.0,,0.0004083111066796693,0.00005923885201534818,0.00032468585368301475,0.0011286480172495361 | |
| 377 | +sparse,S_norobust,mrc,12.0,80,0.02774903810608696,0.02174794181752033,0.00006330901527482267,0.024731429666599004,0.9892361111111111,0.9995762711864407,0.36677202300912076,0.009375000000000003,0.01820567193302796,1.0,,0.0004699649185226993,0.00006231266691008106,0.0003925364803889557,0.00094648343437461 | |
| 378 | +sparse,S_sharp_filtered,mrc,12.0,80,0.029403965548592825,0.022222792320908964,0.00007652918314096955,0.031114369253225794,0.9781770833333333,0.9984286723163841,0.37432233123114356,0.10729166666666667,,1.0,,0.0005035781532055661,0.00008785375484647186,0.0005510335753181809,0.001120900569437922 | |
| 379 | +sparse,K_qhpi,mrc,12.0,80,0.030286109206452173,0.02380366933589394,-0.000023751104374636274,0.03045615418199668,0.7910590277777778,0.8842514124293785,0.37583349074446987,0.007291666666666666,,1.0,,0.0006534541318118027,0.00006276783818019547,0.0005999850306495385,0.005827598090094896 | |
| 380 | +sparse,S_coarseloc,mrc,12.0,80,0.032822336668954656,0.025550520339336073,-0.00005957390641927246,0.028053626427831407,0.9886458333333333,0.9981285310734463,0.32135184488681784,0.08645833333333333,0.019732990889592277,1.0,,0.0008290556107761899,0.00010575283682214744,0.0005662135991965987,0.0009691957993814682 | |
| 381 | +sparse,D_rtd13,mrc,6.1,80,0.08469117462994169,0.06372684786553955,-0.00015552922214576512,0.061028373364307734,,,0.1783254877824616,-0.01353174603174603,,0.9990189318783068,,0.001522505322354529,0.00013697629430032304,0.0010864342676625098, | |
| 382 | +sparse,B_stratified,mrc,11.9875,80,0.09082089185435228,0.07084944334160317,-0.00013062728000715705,0.08153820961117103,,,0.09988642667661307,0.18731060606060607,,0.9857875631313131,,0.0008156993458563231,0.00008228524009268673,0.0007125022984146616, | |
| 383 | +sparse,C_pooled_td,mrc,11.9,80,0.10092106785933723,0.07491831728659135,-0.00004499403492420898,0.07167962572262417,0.9733524545149208,0.9879351715247269,0.16776711182239404,-0.0014015151515151488,,0.9865672348484849,,0.0015867730705241214,0.00012990701092181016,0.001140627480142715,0.0005055098654145223 | |
| 384 | +sparse,A_median,mrc,12.0,80,0.21386330310720908,0.16578443974197743,0.00046230896771924017,0.15172174117436157,,,0.07686372706009183,-0.06979166666666667,,0.9843576388888888,,0.0025740324714309164,0.00029482723883725797,0.0018704852934770012, | |
| 385 | +sparse,H_repeat_sales,mrc,12.0,80,0.38974003823805525,0.30188672195283506,-0.0013927210708543825,0.2750794612043983,,,0.04020939559160148,-0.06129419191919192,,0.6852604166666667,,0.006388150325539042,0.0012557173043767343,0.004329040773337036, | |
| 386 | +sparse,S_noTV,municipality,59.9375,80,0.02402696559587675,0.01883718929801714,0.000023539840557387406,0.030937209692997025,0.9895034722222222,0.999727508857608,0.4235807394854211,0.025081214689265535,0.031652991099822995,1.0,,0.0003996579564364163,0.00005842865743131225,0.000497156984648211,0.0005903601048894891 | |
| 387 | +sparse,S_sharp,municipality,59.9375,80,0.02403589518692043,0.018845664840646877,0.00003542798230492243,0.030965541110548955,0.9895657368173257,0.9997310399310543,0.42318346957473885,0.023841807909604523,0.03162915206291615,1.0,,0.0003991517630780651,0.00005848727366205073,0.0004930674033655672,0.0005687487554608712 | |
| 388 | +sparse,S_LLT,municipality,59.9375,80,0.024471290032103406,0.019173269138301294,0.00006289899021390762,0.04296110338680225,0.9741556026365348,0.9996531169204251,0.41832780726742713,0.024872881355932207,0.06421068248831958,1.0,,0.0003891559876956717,0.00006557130969704465,0.0007341137141423062,0.0010873836968972852 | |
| 389 | +sparse,S_noEB,municipality,59.9375,80,0.024541366494637673,0.019229876956132014,0.00004700433091553358,0.034013781129778826,0.9848072033898305,0.9995787249832423,0.41652843459988975,0.017164548022598872,0.03222810447368365,1.0,,0.00039061046247639074,0.00005828596108793132,0.0005204677000991571,0.0006776948066954708 | |
| 390 | +sparse,S_norobust,municipality,59.9375,80,0.032073101195896986,0.024748010021959622,0.00007363755851680061,0.041062523898419884,0.9840695032956684,0.9988830436656133,0.33679805733535484,0.01768361581920904,0.03950607099355559,1.0,,0.00045847864312246914,0.00006114671091604684,0.0006598278873788353,0.0006849991423218194 | |
| 391 | +sparse,S_coarseloc,municipality,59.9375,80,0.033027199747959854,0.02573512594436227,-0.000059110698887931294,0.03035133477871648,0.9941982109227873,0.9994243153308437,0.3127204451253851,0.08806497175141244,0.03675782581151606,1.0,,0.0008219060357139925,0.00010507059315208037,0.0005288496722678392,0.0005841142756699725 | |
| 392 | +sparse,K_qhpi,municipality,59.9375,80,0.0357013529730138,0.027150548483855057,-0.00005141121239182686,0.05321718792856196,0.8927468808851223,0.9648303289284689,0.3381013963830998,0.008146186440677966,,1.0,,0.00044359934183129984,0.00006317192428744618,0.0005935799448947589,0.001826912380444265 | |
| 393 | +sparse,S_sharp_filtered,municipality,59.9375,80,0.04174033110263995,0.028270817245374048,0.00008233685176649729,0.055825158051636035,0.9763595809792843,0.9989706023173417,0.2897899598743043,0.18611228813559322,,1.0,,0.0005673545687552572,0.00009782740122240573,0.0008254404909825232,0.0007439009674361123 | |
| 394 | +sparse,B_stratified,municipality,33.0375,80,0.055673845776797126,0.04077063347103224,-0.00019250849120720732,0.06871514796955079,,,0.07195577848124776,0.4823663787633346,,0.9555899150445702,,0.00025289391768192927,0.00005377732471591472,0.0002772195903036817, | |
| 395 | +sparse,D_rtd13,municipality,2.730769230769231,78,0.07843443366747398,0.060076437264328424,0.0000556817004356394,0.05769836473702908,,,0.19449764481705006,-0.07045177045177045,,0.9992913105413106,,0.0018545349746200658,0.0001787603260374468,0.00144198730298796, | |
| 396 | +sparse,C_pooled_td,municipality,20.75,80,0.15235339622317612,0.11222841515024835,-0.00011992831602254648,0.1091745132441414,0.9625682732010536,0.9769021265638914,0.11345998498475943,-0.048638670136105344,,0.9310699274144513,,0.0014107262253654081,0.00019786966053938514,0.0009732010364207297,0.0006034692481495871 | |
| 397 | +sparse,A_median,municipality,59.9375,80,0.2943346356221373,0.23357034143713298,0.00048589130984112787,0.20959649489527826,,,0.055137244769098405,-0.006671036524005298,,0.6177504119585688,,0.0012526840410330058,0.0010957762220765819,0.0007079295689627891, | |
| 398 | +sparse,H_repeat_sales,municipality,59.9375,80,0.429727986659016,0.3383286986150994,-0.0018956301817396755,0.30296913645836426,,,0.030103225734542417,0.01405750499500499,,0.09622022128060263,,0.008429756438111197,0.0019067819987528,0.005437485775896917, | |
| 399 | +sparse,C_pooled_td,province,1.0,80,0.02127000043010369,0.016508122469033437,-0.0000341587148314844,0.015089028601365396,0.9804166666666665,0.9970338983050848,0.5053579751896875,0.0,,1.0,0.1261366367340088,0.000425386916021922,0.00005605414820487249,0.00027706155641479043,0.0007717201820486164 | |
| 400 | +sparse,S_noTV,province,1.0,80,0.02134815404455081,0.016567955927985437,-0.000032301329156692064,0.015142052219762416,0.9804166666666665,0.9970338983050848,0.5034359932805265,0.0,,1.0,1.7650156617164612,0.00042543458855475965,0.00005591681713852639,0.0002770600406443239,0.0007717201820486159 | |
| 401 | +sparse,S_noEB,province,1.0,80,0.02136332046841311,0.016578080368841237,-0.00001707694515409391,0.015236842209056875,0.9804166666666665,0.9970338983050848,0.5028213704378081,0.0,,1.0,1.550735005736351,0.0004269842112735038,0.00005649676230473435,0.000276601850273845,0.0007717201820486161 | |
| 402 | +sparse,S_sharp,province,1.0,80,0.02136332046841311,0.016578080368841237,-0.000017076945154093917,0.015236842209056875,0.9804166666666665,0.9970338983050848,0.5028213704378081,0.0,,1.0,1.7666853457689284,0.0004269842112735038,0.00005649676230473435,0.00027660185027384506,0.0007717201820486167 | |
| 403 | +sparse,S_LLT,province,1.0,80,0.02136332046841311,0.016578080368841237,-0.00001707694515409391,0.015236842209056875,0.9804166666666665,0.9970338983050848,0.5028213704378081,0.0,,1.0,12.655669629573822,0.0004269842112735038,0.00005649676230473435,0.00027660185027384506,0.0007717201820486187 | |
| 404 | +sparse,S_sharp_bottomup,province,1.0,80,0.021488236346125573,0.016674196188764253,-3.743399045334011e-6,0.015592190229003872,,,0.4998342015849297,0.0,,1.0,,0.0004261856220897005,0.000055750047281389314,0.00028934233998996874, | |
| 405 | +sparse,K_qhpi,province,1.0,80,0.02363256240872542,0.018416913235448945,-0.00008733677570947408,0.01697328810217478,,,0.4637096634958069,0.0,,1.0,4.27731055021286,0.00046448799545365486,0.00006110107791667445,0.00030099924770406097, | |
| 406 | +sparse,D_rtd13,province,1.0,80,0.02363256240872542,0.018416913235448945,-0.00008733677570947406,0.01697328810217478,,,0.463709663495807,0.0,,1.0,3.944505748152733,0.00046448799545365465,0.00006110107791667445,0.00030099924770406097, | |
| 407 | +sparse,S_norobust,province,1.0,80,0.02619203934822754,0.02038926242423399,0.000019171836497397557,0.01862664944258781,0.9814583333333333,0.998728813559322,0.41576848432706515,-0.0125,,1.0,1.0486032664775848,0.00048096940745038944,0.00006120388237900052,0.0003331752544623531,0.000662549921161372 | |
| 408 | +sparse,S_coarseloc,province,1.0,80,0.03180501416306393,0.024635010107222098,-0.00008491883699543806,0.022846525054382883,0.9799999999999999,0.9951271186440678,0.3674231430553699,0.0625,,1.0,1.763926586508751,0.000851544182883214,0.00010496723010769384,0.0006112866211374139,0.0009090109925236518 | |
| 409 | +sparse,E_fisher_chained,province,1.0,80,0.03809064599443414,0.029265926558390425,-0.00023646444190484275,0.04755136984119716,,,0.3100654125190295,0.0,,0.9935416666666667,3.865687909722328,0.001022071430991994,0.00023995579828109916,0.00247113828277026, | |
| 410 | +sparse,B_stratified,province,1.0,80,0.05206490149382252,0.040545401684615935,0.00006323451582848608,0.03745009688564647,,,0.22794059614559958,-0.0375,,1.0,0.00915129780769348,0.001259248763494842,0.00014450020465973343,0.0009065496768548239, | |
| 411 | +sparse,A_median,province,1.0,80,0.0607489288540277,0.04734628323455432,-0.000058352957226677385,0.04299483359544202,,,0.20129033750834532,0.1,,1.0,0.003843751549720764,0.001280635657742595,0.00017810555801318743,0.000893334750833972, | |
| 412 | +sparse,H_repeat_sales,province,1.0,80,0.09568984283426377,0.07319252143153784,-0.00008011123069442817,0.06663614935638382,,,0.12274354074967578,-0.1,,1.0,0.023673206567764282,0.0020756903644331744,0.00021786854278583887,0.001313234448376378, | |
| 413 | +sparse,K_qhpi,region,4.0,80,0.021406118124319203,0.01692054419591927,-0.00009682889776734311,0.01927275304136075,0.7887500000000001,0.8589512711864407,0.4347061564092819,0.03125,,1.0,,0.0005560545892856844,0.000050089646125711854,0.000393813265509992,0.012086924753633573 | |
| 414 | +sparse,S_LLT,region,4.0,80,0.022299547644394256,0.017419416774991335,0.00002110622257509997,0.01870791469567717,0.9851041666666667,0.9980932203389831,0.455779257800104,0.003125,0.012412250514152044,1.0,,0.0004032142655510137,0.00005986164146296683,0.0002900958439426349,0.0012286659568017918 | |
| 415 | +sparse,S_noTV,region,4.0,80,0.02234467252200617,0.0174713988389299,-0.000011113923559739516,0.018553191110459076,0.9884375000000001,0.9981991525423728,0.45862777171587965,0.00625,0.008084077892687733,1.0,,0.00040465331247992826,0.000057169307329656754,0.0002994631620063762,0.0010611961471180672 | |
| 416 | +sparse,S_sharp,region,4.0,80,0.022356158603552636,0.017483425112314394,3.287351544053324e-6,0.01861086386775803,0.98828125,0.9982521186440678,0.4581467587701965,0.00625,0.008096483080743481,1.0,,0.0004057864738598863,0.00005753481113883232,0.00029520866629937866,0.0010743648311202785 | |
| 417 | +sparse,S_noEB,region,4.0,80,0.022360627798309437,0.01748379732895664,5.429989768768858e-6,0.01869947372380884,0.985625,0.9979872881355932,0.45575649819492314,0.00625,0.0073173035122814836,1.0,,0.0004025133032851223,0.00005785168364007353,0.0002903772934941931,0.001183396102810161 | |
| 418 | +sparse,S_sharp_filtered,region,4.0,80,0.02435012062416608,0.019115988659471828,0.00004064307128954163,0.021581474839458353,0.9876041666666667,0.9981991525423728,0.4349666502284868,0.0625,,1.0,,0.0004274001338510258,0.00006628117467216982,0.0003693398280461601,0.0009663573226478708 | |
| 419 | +sparse,S_norobust,region,4.0,80,0.02695217256348865,0.021075737957469955,0.0000457719414522696,0.02120519281964111,0.991875,0.9992055084745763,0.38115740300485984,0.01875,0.008145643850423332,1.0,,0.00045646070119749856,0.00006195155751868339,0.0003290793749246905,0.0009105207278149097 | |
| 420 | +sparse,S_coarseloc,region,4.0,80,0.03238335141918784,0.025187376621441493,-0.00006637464100638565,0.025439794559132776,0.9880729166666666,0.996927966101695,0.33333578835988453,0.125,0.008581814453025088,1.0,,0.0008312563820938032,0.00010445024272897089,0.0005802833729170355,0.0011062364914508945 | |
| 421 | +sparse,C_pooled_td,region,4.0,80,0.04638103456687295,0.03590228859912203,0.000016281284731686242,0.03290187018589879,0.9789583333333333,0.9950741525423729,0.30267755178975164,0.0375,,1.0,,0.0007465247189561772,0.0000695709715415132,0.0005275397853514072,0.0004622958742913252 | |
| 422 | +sparse,D_rtd13,region,4.0,80,0.05445682234614,0.042221511067511416,-2.7850001769970986e-6,0.03928253786074031,,,0.2625792616249091,0.009375,,1.0,,0.0009116798221315534,0.00008297609144414634,0.0006677752720757687, | |
| 423 | +sparse,B_stratified,region,4.0,80,0.09117519632629695,0.07178465322212454,0.000042185214365366675,0.0674519178709994,,,0.1518531498448133,0.0125,,0.9989583333333334,,0.0015740516212356095,0.00011958992984787626,0.0012452125702025446, | |
| 424 | +sparse,A_median,region,4.0,80,0.12369816241655099,0.09616672425056208,-0.00003592097735290571,0.08718128988227633,,,0.1161510889107287,0.05625,,1.0,,0.002575113301028042,0.00019689781730437942,0.0018414675413195034, | |
| 425 | +sparse,H_repeat_sales,region,4.0,80,0.22215238693267225,0.16700108844454106,-0.0002310126463382859,0.1569118869006344,,,0.06885451656535017,-0.059375,,0.9665625,,0.003934193955718292,0.0005237530395209104,0.0026907178400727857, | |
| 426 | +turning_point,S_LLT,mrc,12.0,80,0.01451311381913491,0.011503559820644316,0.000025966266502514344,0.015169438655194364,0.9724479166666666,0.9988524011299434,0.6748127271809446,0.00625,0.018317008556407336,1.0,,0.00021400126275666052,0.000037317084027634706,0.0001751482615082277,0.0012386783513900815 | |
| 427 | +turning_point,S_sharp,mrc,12.0,80,0.01460579932112636,0.011589915130972317,0.000025141155312569746,0.014891299038037523,0.9819444444444445,0.9994526836158192,0.675355596888296,0.004166666666666667,0.011078265634521143,1.0,,0.00021551793423144232,0.00003654772626977521,0.0001746410895452688,0.0009492205700345151 | |
| 428 | +turning_point,S_noTV,mrc,12.0,80,0.01460770549535303,0.01158975139771511,0.000015299738801005027,0.014871903710300666,0.9816145833333334,0.9994350282485875,0.6752445129567182,0.004166666666666667,0.011076104614761008,1.0,,0.0002152821459342722,0.00003600014165203889,0.00017340480600232187,0.0009412264942077304 | |
| 429 | +turning_point,S_noEB,mrc,12.0,80,0.014622675089077266,0.011600389920437864,0.000026238616506313907,0.015132009040579536,0.9740104166666667,0.9990112994350282,0.6735507953536869,0.0010416666666666664,0.010500392476733013,1.0,,0.00021454532986909928,0.000037004179368204256,0.0001778014969825974,0.0012119627153377654 | |
| 430 | +turning_point,S_norobust,mrc,12.0,80,0.01768884522071703,0.013954659574601078,0.00001834804175784795,0.016472618946644473,0.9880555555555555,0.9990289548022598,0.6030304640709935,0.002083333333333333,0.01223841562438746,1.0,,0.00029004436172382173,0.000042148175086604866,0.00021072530579056828,0.0008424402756666642 | |
| 431 | +turning_point,K_qhpi,mrc,12.0,80,0.018659410110567674,0.014817959610601469,0.00002338021316903313,0.019420263324822766,0.8758506944444445,0.9603283898305085,0.5812027200310089,-0.015625,,1.0,,0.00033805369846594377,0.00003709809861517092,0.0003086084596869081,0.004121257909222237 | |
| 432 | +turning_point,S_sharp_filtered,mrc,12.0,80,0.019370547538120078,0.015068761367110859,0.000031951112329129816,0.020369731798874878,0.9702604166666667,0.9986052259887005,0.583762917291339,0.057291666666666664,,1.0,,0.00026129360240056775,0.00004797756510045754,0.0002711807903660175,0.0010957016889253403 | |
| 433 | +turning_point,S_coarseloc,mrc,12.0,80,0.020147041504941437,0.015768210813789412,0.00011070907373951569,0.01853866986285098,0.98671875,0.9979166666666666,0.5518001437176427,0.013541666666666665,0.013076881545571345,1.0,,0.00041640471245502417,0.00005310666572648321,0.00028774616903302225,0.0010225459569953058 | |
| 434 | +turning_point,C_pooled_td,mrc,12.0,80,0.05400356494437021,0.04139706209514107,0.0000402539544446299,0.0383195503324162,0.9783443332379589,0.9940226569487628,0.3134499781146295,0.008333333333333335,,0.9993055555555556,,0.0008578893973480207,0.00005338217902961887,0.0006055980951234083,0.00030212740871728177 | |
| 435 | +turning_point,D_rtd13,mrc,11.2875,80,0.05917305303349746,0.04545989621654191,0.000028136986358979685,0.04270878888302163,,,0.28858674547343155,-0.026041666666666664,,0.9998892045454545,,0.0008548473637488757,0.00005840331811197744,0.0006060153747124992, | |
| 436 | +turning_point,B_stratified,mrc,12.0,80,0.08468277855388488,0.06668302619579096,0.0002023758537918691,0.06529103585295577,,,0.18688735840182297,0.05625,,0.996857638888889,,0.0008611069079232448,0.00007309860098513936,0.0007104050360103524, | |
| 437 | +turning_point,A_median,mrc,12.0,80,0.1299928045690438,0.1010042340969924,-0.000010583854556435465,0.0922352263111405,,,0.14226554578929712,0.08229166666666667,,0.9993055555555556,,0.0020160391331075196,0.0001210004023049269,0.0014327679664532889, | |
| 438 | +turning_point,H_repeat_sales,mrc,12.0,80,0.2515503220711658,0.19104722971626484,-0.00033392789875692503,0.17898386256610238,,,0.07005742748031332,-0.04566287878787879,,0.9325520833333334,,0.003488506598927595,0.00043524370445066765,0.002542686990081474, | |
| 439 | +turning_point,S_sharp,municipality,60.0,80,0.015767583366925103,0.0125044406347911,0.000027787239995201446,0.02226767132483677,0.9838055555555556,0.9998764124293785,0.6498872000676221,0.0025,0.023648978529205916,1.0,,0.000218092085358302,0.00004014949855295311,0.0003339941299079544,0.0006090298275396197 | |
| 440 | +turning_point,S_noTV,municipality,60.0,80,0.01578150824266779,0.012513211563928187,0.000018161419939420312,0.02227974103447314,0.9837430555555555,0.9998658192090396,0.6496286921241247,0.003125,0.02366667867587144,1.0,,0.00021796029043657898,0.000039720761493574746,0.0003333379650538746,0.0006151845881302964 | |
| 441 | +turning_point,S_LLT,municipality,60.0,80,0.016092097792137917,0.012740830061633226,0.00003369330821232247,0.028255963964214786,0.9674305555555556,0.9997669491525423,0.6445820517726946,0.009375,0.04570115383649296,1.0,,0.0002159579024704673,0.000045979442697689835,0.0004340550304747097,0.0008997433586438193 | |
| 442 | +turning_point,S_noEB,municipality,60.0,80,0.016182591187484108,0.012821917314295,0.00002724744841683624,0.023882126490996212,0.9749236111111111,0.9997139830508475,0.6425265147314181,0.007916666666666666,0.02374668716062932,1.0,,0.0002137517786218343,0.000041729471396639625,0.00034303544880442483,0.0007957256930713284 | |
| 443 | +turning_point,S_coarseloc,municipality,60.0,80,0.020456591347189534,0.01603237362168504,0.00010692589497123194,0.020958627534755844,0.9940520833333334,0.9995727401129944,0.5397866655904734,0.015,0.026703889317458068,1.0,,0.00040818430097698213,0.00005076350047582465,0.00024852110124399656,0.00032466059271038153 | |
| 444 | +turning_point,S_norobust,municipality,60.0,80,0.02245111332442156,0.01732313184369735,0.000038537916393059926,0.03028303803164658,0.9795138888888889,0.9982803672316385,0.5508571592466123,0.0027083333333333343,0.030366838522902877,1.0,,0.00037571698571373574,0.00004995930971391215,0.000561227492103961,0.0006943477429155306 | |
| 445 | +turning_point,K_qhpi,municipality,60.0,80,0.029140900613530835,0.022116884386350476,-4.37255001923933e-6,0.04251612593099319,0.8858819444444445,0.9753460451977402,0.5106102521384371,0.022291666666666664,,1.0,,0.0004615999326668889,0.00004751079536910683,0.0006232146887907187,0.002152115699424264 | |
| 446 | +turning_point,S_sharp_filtered,municipality,60.0,80,0.030152879628772188,0.020591640129909588,0.000010466624616455998,0.039733583392060165,0.9686319444444444,0.9989477401129943,0.44881220028626145,0.19708333333333333,,1.0,,0.00037249002946639043,0.00006185021210182078,0.0005354064024768379,0.0007169996548394218 | |
| 447 | +turning_point,B_stratified,municipality,49.1875,80,0.06020114288171422,0.04525488870447076,0.0005110812142116913,0.0671293908034988,,,0.1401850943143565,0.4586332861310377,,0.9679143623352203,,0.00018561525330373666,0.00003692336691976295,0.00026892633680046995, | |
| 448 | +turning_point,D_rtd13,municipality,11.8375,80,0.07476284096538907,0.056715721698853735,0.000018144414375463726,0.053987216372636794,,,0.23696060130782617,-0.009952335246452892,,0.9994519916188114,,0.0009692162383357509,0.00005928587657588467,0.0006597105945053193, | |
| 449 | +turning_point,C_pooled_td,municipality,39.2625,80,0.1252157897238662,0.09244075507784523,-0.00017891515965716258,0.08980686139210688,0.9677454742029962,0.9830515584734176,0.16463308817328834,0.03212743122433982,,0.9565275781612005,,0.0010060644047377245,0.00011908793623940341,0.0006852851985842374,0.00038960254079764296 | |
| 450 | +turning_point,A_median,municipality,60.0,80,0.24275115114555348,0.19000019532092757,-0.0004303374215955663,0.17293378571644794,,,0.0808288723763148,-0.014583625416999004,,0.815420138888889,,0.0016306233554351916,0.0006524609448272028,0.0011639571253097745, | |
| 451 | +turning_point,H_repeat_sales,municipality,60.0,80,0.3923012636206997,0.3049755069754182,0.00033695312993570263,0.27615394367977475,,,0.0376837339806456,-0.040067288947627795,,0.3024444444444444,,0.004552227736652953,0.0008787996543152386,0.0029207323450650986, | |
| 452 | +turning_point,C_pooled_td,province,1.0,80,0.012424617420623346,0.00974533086839181,6.429501457142756e-7,0.008796330745568261,0.9806250000000001,0.9970338983050848,0.750496366300571,0.0,,1.0,0.33525592684745786,0.0002475761799787312,0.00003173756523304782,0.0001651555780300848,0.0008089116640083596 | |
| 453 | +turning_point,S_sharp,province,1.0,80,0.01247423807450658,0.009787756621104123,0.000010686954839875933,0.008848463830013045,0.9806250000000001,0.9972457627118644,0.7491195438466811,0.0,,1.0,2.720532011985779,0.00024882634042190335,0.000032052795935943724,0.000167243116542751,0.00080891166400836 | |
| 454 | +turning_point,S_LLT,province,1.0,80,0.01247423807450658,0.009787756621104123,0.000010686954839875931,0.008848463830013045,0.9806250000000001,0.9972457627118644,0.7491195438466811,0.0,,1.0,559.6254273414612,0.0002488263404219033,0.000032052795935943724,0.00016724311654275098,0.00080891166400836 | |
| 455 | +turning_point,S_noEB,province,1.0,80,0.01247423807450658,0.009787756621104123,0.000010686954839875934,0.008848463830013045,0.9806250000000001,0.9972457627118644,0.7491195438466811,0.0,,1.0,545.7131586194039,0.0002488263404219034,0.00003205279593594373,0.000167243116542751,0.0008089116640083593 | |
| 456 | +turning_point,S_noTV,province,1.0,80,0.012477884249311113,0.009785574434437911,1.0556835301900488e-7,0.00883238501072057,0.9808333333333333,0.9972457627118644,0.7489870552608755,0.0,,1.0,2.6963600665330887,0.0002484544431242723,0.000031836118198283475,0.00016595977805586857,0.0007900132634326039 | |
| 457 | +turning_point,S_sharp_bottomup,province,1.0,80,0.012641800624443481,0.009931788244530521,0.000012240064984904632,0.009218023589517182,,,0.7448585530749579,0.0,,1.0,,0.00024835178361966857,0.00003488658716527301,0.00017203379376179735, | |
| 458 | +turning_point,D_rtd13,province,1.0,80,0.013914067442239808,0.010857995506017324,-0.00002642076226693127,0.010055530504417468,,,0.71565098566065,0.0,,1.0,6.301649501919746,0.00027889291630275946,0.00003838980035991139,0.0001923398064226035, | |
| 459 | +turning_point,K_qhpi,province,1.0,80,0.013914067442239808,0.010857995506017324,-0.00002642076226693127,0.010055530504417468,,,0.71565098566065,0.0,,1.0,6.772559919953347,0.00027889291630275957,0.0000383898003599114,0.0001923398064226035, | |
| 460 | +turning_point,S_norobust,province,1.0,80,0.01586899905712983,0.012420166728756205,-2.617533742397163e-6,0.011170690851616642,0.9808333333333333,0.9972457627118644,0.6644718224709497,0.0,,1.0,1088.4333150088787,0.0003074568836269104,0.000040273987420940754,0.0002022665780747789,0.0006695612266902206 | |
| 461 | +turning_point,S_coarseloc,province,1.0,80,0.0187539052172652,0.014579574565886913,0.00009277909247998508,0.013394088251267824,0.9804166666666667,0.9955508474576271,0.6103764532848526,0.0,,1.0,2.7604000210762023,0.0004443467958473726,0.00005018300298875068,0.0003198813091665543,0.0007124939854657675 | |
| 462 | +turning_point,E_fisher_chained,province,1.0,80,0.021149408488301755,0.0164283673115882,2.021822232490682e-6,0.018016593045088104,,,0.5626911142319242,0.0125,,1.0,5.400713968276977,0.0004709861063678163,0.00007112097598532374,0.000519474724326817, | |
| 463 | +turning_point,B_stratified,province,1.0,80,0.030775052536997534,0.024056527458482396,0.0002377177901692251,0.02217415475976133,,,0.4326752713068823,0.0,,1.0,0.017784488201141358,0.0007200383897527029,0.0000816693316258786,0.0005253135422176165, | |
| 464 | +turning_point,A_median,province,1.0,80,0.035610223922236145,0.027962339142070025,0.00009881295631894412,0.025596197429553776,,,0.38176221179220443,0.0875,,1.0,0.0075467050075531,0.0007938983458593679,0.00010582763603244666,0.0005671147447246739, | |
| 465 | +turning_point,H_repeat_sales,province,1.0,80,0.05322541044246106,0.0409570436401201,-0.00006682526088799511,0.03726217265163227,,,0.24943998887001168,-0.075,,1.0,0.05961587131023407,0.0009941041167343856,0.00016289973758812454,0.0006939940821108241, | |
| 466 | +turning_point,K_qhpi,region,4.0,80,0.012327501135602676,0.009852869597895919,0.00007083766077226644,0.011579166086178592,0.9378645833333333,0.9824152542372883,0.6633039871992783,-0.003125,,1.0,,0.00012185881471797059,0.000024136309404669156,0.00013175781944505002,0.0021990462182396877 | |
| 467 | +turning_point,S_LLT,region,4.0,80,0.013907890377218304,0.011011313910702508,0.000021625010724362686,0.012131835792265958,0.9836458333333333,0.9983580508474577,0.695746363664856,0.00625,0.008171566270006442,1.0,,0.00021929872157756987,0.00003657930639221658,0.00016998226745097714,0.0011761196860110741 | |
| 468 | +turning_point,S_noEB,region,4.0,80,0.013941926087476214,0.011042028143162673,0.00001699507730618164,0.012082799771049379,0.9838020833333333,0.9985699152542373,0.6954504096706308,0.00625,0.005264082740635428,1.0,,0.00022005347356403123,0.00003535097928792262,0.00016681898938628753,0.001133828438601775 | |
| 469 | +turning_point,S_sharp,region,4.0,80,0.01398978686914088,0.011079688385874753,0.000015211994380255234,0.012086056219504933,0.98703125,0.9989406779661018,0.6961162164007022,0.003125,0.005868524630210711,1.0,,0.00022104569117695056,0.00003537823513868284,0.0001660361965499082,0.0009433966864097935 | |
| 470 | +turning_point,S_noTV,region,4.0,80,0.013993298459806708,0.011080318136332225,4.921046085822676e-6,0.012076648752689888,0.9869791666666666,0.9988877118644067,0.6959682474488289,0.003125,0.00587110309676314,1.0,,0.000220795832706829,0.00003507209689586414,0.00016530641074370014,0.0009474101152679127 | |
| 471 | +turning_point,S_sharp_filtered,region,4.0,80,0.015791871531900585,0.012554474204668136,0.00002580951400727718,0.013958074654676595,0.9845312500000001,0.9978813559322035,0.659622576776934,0.00625,,1.0,,0.00024054389502931907,0.00003754482045391541,0.00020702574612419043,0.000994981666913003 | |
| 472 | +turning_point,S_norobust,region,4.0,80,0.016973232172239134,0.013362198138029427,7.452014072610089e-6,0.013607381289341549,0.990625,0.998728813559322,0.6227240658864396,0.00625,0.005966470017250998,1.0,,0.0002838443116648053,0.000041443978104137434,0.0001975268741872006,0.0007931365435110159 | |
| 473 | +turning_point,S_coarseloc,region,4.0,80,0.019696544314660322,0.015394533821235096,0.00010157310690532826,0.015958361058682525,0.9884375000000001,0.9962923728813559,0.5680533604533613,0.00625,0.006069651845099761,1.0,,0.000418664196040132,0.00005176093233148085,0.00030195439035603195,0.0009998873661954922 | |
| 474 | +turning_point,C_pooled_td,region,4.0,80,0.02629274698919578,0.02047792181374268,0.000025578849888532012,0.018640569930321517,0.9795833333333333,0.9953389830508474,0.5142918301605424,-0.00625,,1.0,,0.0003999808833883479,0.00003567175348963033,0.00027761252421387706,0.00041666666666666496 | |
| 475 | +turning_point,D_rtd13,region,4.0,80,0.02990535103294854,0.023235662315547908,9.69123060024418e-7,0.021460267851450505,,,0.4716657155827537,-0.009375,,1.0,,0.00047890588841801554,0.00004287986824856814,0.0003334180172070027, | |
| 476 | +turning_point,B_stratified,region,4.0,80,0.060283712103543,0.04712772130338767,0.00010657548914363325,0.043157188452249626,,,0.2637942064092434,-0.003125,,1.0,,0.0013288023018119559,0.00008446030084357568,0.0009301932249663722, | |
| 477 | +turning_point,A_median,region,4.0,80,0.07242324664816284,0.056500341759534015,0.00010830128421148828,0.05137977772365278,,,0.23057903451205392,0.1125,,1.0,,0.0015669153082677675,0.00009534283339530109,0.001072860060664658, | |
| 478 | +turning_point,H_repeat_sales,region,4.0,80,0.12030898678586059,0.09120194156729268,0.00005924562966996715,0.08476925913182576,,,0.13026048217414837,0.00625,,0.9985416666666668,,0.002056634438046059,0.0001921184793358671,0.0014152679657792777, | |
added
results/vintages/revision_policies_province_filtered.csv
+4 −0
@@ -0,0 +1,4 @@ | ||
| 1 | +class,months,mae_first_release_vs_final,bias_first_release_vs_final,mae_third_release_vs_final,rmse_first,rmse_third,mean_abs_true_change | |
| 2 | +condo,52,0.0015620242064084064,0.00012999474800442522,0.0015485661521688215,0.0020789246210967667,0.0020302944477716685,0.007675301455052519 | |
| 3 | +house,52,0.0015532707391587841,-0.00012648911680931643,0.0015806828848220328,0.0020166096637717987,0.001978731246833613,0.015036741431184 | |
| 4 | +plex,52,0.0025609731874916717,-0.00013590920564457217,0.0025993659546795556,0.0035467549510441792,0.0035119400625493245,0.015285921387441144 | |
modified
results/vintages/revisions_by_release.csv
+72 −72
@@ -1,16 +1,16 @@ | ||
| 1 | 1 | class,level,release,obs,mae_rev_m1_vs_final,bias_rev_m1_vs_final,mae_rev_12m_vs_final,bias_rev_12m_vs_final,mae_rev_m1_vs_last_rt,mae_rev_12m_vs_last_rt,share_m1_rev_gt_1pct |
| 2 | −condo,municipality,1,3326,0.018218959210363525,0.000016688468351380448,0.035899729145987345,-0.00021023850922487306,0.010687310523249133,0.018543645860963492,0.539386650631389 | |
| 3 | −condo,municipality,2,3326,0.01830369137665762,0.0003420352663881496,0.035203232880077505,0.000014803896699086845,0.010651332141485633,0.01800571985494987,0.5426939266386049 | |
| 4 | −condo,municipality,3,3326,0.018253256323750244,0.00016979601424385027,0.034917315515444566,-0.00007206650223968797,0.010565016295267286,0.017769186486300787,0.5469031870114252 | |
| 5 | −condo,municipality,4,3326,0.018498001562029184,-0.00023268101188767977,0.03468796870069724,-0.00006589479040444394,0.010597045308313584,0.017713744598356427,0.5463018641010222 | |
| 6 | −condo,municipality,5,3326,0.01890243926725158,0.00021581986863248198,0.035178307015601325,-0.00016159941438881678,0.010842865379196508,0.017952100712717328,0.550210463018641 | |
| 7 | −condo,municipality,6,3326,0.01881462255308852,0.00028753995744244874,0.03532503607865267,-0.000701690943807063,0.01091535931184554,0.018263884216127575,0.5493084786530367 | |
| 8 | −condo,municipality,7,3326,0.018671778319748422,0.0003328626747500349,0.036041158805345176,-0.0008354496235605425,0.010687919474979822,0.018484741694745257,0.5472038484666266 | |
| 9 | −condo,municipality,8,3326,0.01853143947049999,-0.00016892757131686554,0.03586994538523937,-0.000523705132569303,0.010673162772855687,0.01829841904002192,0.544798556825015 | |
| 10 | −condo,municipality,9,3326,0.01858009111707426,-0.0003449297154774869,0.03545922010754932,-0.00018396812920626674,0.01050755377992387,0.017667555346076835,0.5514131088394468 | |
| 11 | −condo,municipality,10,3326,0.0185982825211036,-0.00032078107918533257,0.034781251007409114,0.00034764241879270803,0.01036657598267548,0.017125414330917934,0.5472038484666266 | |
| 12 | −condo,municipality,11,3326,0.018560632418666984,-0.00042042842032594265,0.034129410672541334,0.0011552891842648084,0.010121351342934837,0.016301168671367203,0.5532170775706554 | |
| 13 | −condo,municipality,12,3326,0.018713291616592024,-0.00008723296083991139,0.033784122050478343,0.001846917023178393,0.01008536273096997,0.016165427891360936,0.5544197233914612 | |
| 2 | +condo,municipality,1,3326,0.008589314141422758,-0.00010791105708893169,0.02400793699992309,-0.0010568634045292674,0.00865325841034632,0.02408898198410686,0.26638604930847865 | |
| 3 | +condo,municipality,2,3326,0.007758566947491185,0.0001845953421532487,0.02091047046645584,-0.0010163633218203314,0.007664896380627846,0.02058289629419175,0.22098616957306072 | |
| 4 | +condo,municipality,3,3326,0.007403160309147861,-0.000012445873828386801,0.019302302170179753,-0.0014568854204114075,0.007206137989370001,0.01868140579198119,0.21136500300661457 | |
| 5 | +condo,municipality,4,3326,0.0073317335436995125,-0.00007564248873277648,0.018202907038347812,-0.0016841313825952482,0.007147188947783235,0.017407414146054487,0.21196632591701745 | |
| 6 | +condo,municipality,5,3326,0.00723055193591952,0.00008715393904671858,0.01753729235082918,-0.0018715747830878385,0.00701618231283182,0.01678006062460107,0.20565243535778713 | |
| 7 | +condo,municipality,6,3326,0.00728449115328191,0.00020464425134900658,0.017240168994935693,-0.0022478812004072483,0.007059055370938091,0.01637572227306755,0.21226698737221888 | |
| 8 | +condo,municipality,7,3326,0.007187878836240458,0.00008210742432695138,0.016954136440850972,-0.002762462857682524,0.00694322606530947,0.016003628697887413,0.20444978953698137 | |
| 9 | +condo,municipality,8,3326,0.0071102949839302034,-0.00003916549583711182,0.016404307467842178,-0.0026596512241069533,0.006826792236285296,0.015347240125737953,0.20114251352976548 | |
| 10 | +condo,municipality,9,3326,0.006952592552038752,-0.00021058050215593698,0.015954932139964784,-0.002565588615647554,0.006625424340301049,0.014757625678969408,0.19092002405291641 | |
| 11 | +condo,municipality,10,3326,0.006716176107547116,-0.0004055666126787601,0.015390770853098862,-0.0022627202769495416,0.006423586224321137,0.014115269123229898,0.1867107636800962 | |
| 12 | +condo,municipality,11,3326,0.006576326417715387,-0.0004411590193217436,0.014843436805319507,-0.0017177530697808615,0.00625605342493804,0.01338237993206276,0.1831028262176789 | |
| 13 | +condo,municipality,12,3326,0.006551006295975159,-0.0003228933117615451,0.014428137047256422,-0.0011114746326732182,0.00624640420258526,0.012863054851450829,0.1782922429344558 | |
| 14 | 14 | condo,province,1,52,0.0015620242064084064,0.00012999474800442522,0.0024936086388600643,0.002180781249067779,0.0015620242064083995,0.0024936086388600443,0.0 |
| 15 | 15 | condo,province,2,52,0.001554788940591851,0.00021421155617977366,0.0024556995539281204,0.0021945097556216576,0.0015547889405918415,0.002455699553928104,0.0 |
| 16 | 16 | condo,province,3,52,0.0016383411282475647,0.00007172615245495256,0.002385968263163059,0.002127974502410657,0.00163834112824756,0.0023859682631630474,0.0 |
@@ -23,30 +23,30 @@ condo,province,9,52,0.001684486401976774,0.0001661810791386928,0.002174010931892 | ||
| 23 | 23 | condo,province,10,52,0.001593885852531655,0.00016859658055825427,0.002117012954797542,0.0018047875724924965,0.0015938858525316494,0.0021170129547975255,0.0 |
| 24 | 24 | condo,province,11,52,0.0016030353170648113,0.00016609797181298672,0.001982236262739768,0.0017272436940498302,0.0016030353170648011,0.001982236262739752,0.0 |
| 25 | 25 | condo,province,12,52,0.0015979690610675432,0.00020430782104002075,0.002030825256681998,0.0017622010900594173,0.0015979690610675526,0.002030825256681985,0.0 |
| 26 | −condo,region,1,832,0.009790266447409655,-0.000031104480999510837,0.019273842101740615,-0.0034347501066969784,0.006077331017071481,0.011545513715423313,0.34134615384615385 | |
| 27 | −condo,region,2,832,0.009842418822069127,-0.00005817544655845735,0.01915099154614561,-0.003793582520574911,0.005901925322208566,0.011257364932724868,0.3401442307692308 | |
| 28 | −condo,region,3,832,0.009979287810595174,-0.00023492539186820973,0.01927819307081639,-0.004153633182955936,0.006011492519209637,0.011521417882571442,0.34134615384615385 | |
| 29 | −condo,region,4,832,0.010126823709202846,-0.00033397566967972305,0.019392051992608312,-0.004460492510153581,0.006075055890613615,0.01172819635641147,0.35216346153846156 | |
| 30 | −condo,region,5,832,0.010179366354186963,-0.0002865979063255834,0.019793882996838732,-0.0047910316429116825,0.006048436736559418,0.012013468400404903,0.36177884615384615 | |
| 31 | −condo,region,6,832,0.010367298598993564,-0.00022179320681323317,0.02002685451109216,-0.005158897833503317,0.006257140672470927,0.012170491393923113,0.3701923076923077 | |
| 32 | −condo,region,7,832,0.010192976572009942,-0.0001287222355680675,0.020767326355680602,-0.00459329080163186,0.006123962972048171,0.012422729646467559,0.36538461538461536 | |
| 33 | −condo,region,8,832,0.01020605005605612,-0.0004294500437435218,0.020638108116871796,-0.00445979331812907,0.006134809678767996,0.012179908602905022,0.35216346153846156 | |
| 34 | −condo,region,9,832,0.010106086957893909,-0.0004738868148322336,0.02058157780165701,-0.004521995638758401,0.006081644606522165,0.011882547908836187,0.34134615384615385 | |
| 35 | −condo,region,10,832,0.01008205418619355,-0.00033571880156921973,0.02034217407559042,-0.004484759347404402,0.005893884835961093,0.011589247146868181,0.34375 | |
| 36 | −condo,region,11,832,0.00999206557050614,-0.00041264004127453417,0.020295766446709166,-0.004603531125417201,0.005814847463540111,0.011335083931070078,0.3401442307692308 | |
| 37 | −condo,region,12,832,0.009847622079466455,-0.00048776006746468407,0.02003105454023685,-0.0044264189327692435,0.0056792893388342485,0.01112211780606469,0.33413461538461536 | |
| 38 | −house,municipality,1,3328,0.023092188875919885,-0.00010638684910900311,0.024208956837254495,-0.0009847268772388564,0.01611997040419284,0.017647401865411975,0.6538461538461539 | |
| 39 | −house,municipality,2,3328,0.02305826378044436,0.0000776618071806769,0.024121334462707294,-0.000733534018982744,0.016076172463857133,0.01754601518602517,0.6607572115384616 | |
| 40 | −house,municipality,3,3328,0.023314129313209635,-0.00018437102168174861,0.0243558017900071,-0.000805528223827719,0.016161186990447114,0.017631539281284643,0.6574519230769231 | |
| 41 | −house,municipality,4,3328,0.023094714275644447,-0.00001242905734893477,0.024438727072204902,-0.0007450148449550792,0.015984483975738917,0.017493185039469124,0.6487379807692307 | |
| 42 | −house,municipality,5,3328,0.02319062035455638,-0.00021508974361102599,0.024551891295953435,-0.0008033047850721947,0.016033372472342513,0.017595766690442386,0.6544471153846154 | |
| 43 | −house,municipality,6,3328,0.023469611454049084,0.00006959664267645442,0.024778345097935032,-0.0008921591898266268,0.016046905816510533,0.0178106438497968,0.6556490384615384 | |
| 44 | −house,municipality,7,3328,0.023757462631077236,-0.00008859729964317513,0.02580790419931541,-0.0010644822404534072,0.01616931963135506,0.017893589258424866,0.6526442307692307 | |
| 45 | −house,municipality,8,3328,0.02405027377329403,0.00003678475982662613,0.025928003607596735,-0.0009488613149150432,0.016375509629565892,0.017865497478796667,0.6589543269230769 | |
| 46 | −house,municipality,9,3328,0.024112394613001943,-0.0003514411949470708,0.025740121764867223,-0.0008296229136360721,0.016516847008696097,0.017721469248294538,0.6613581730769231 | |
| 47 | −house,municipality,10,3328,0.02415853926255957,-0.00005750907250976803,0.025534692908016896,-0.000630725627465175,0.016674363998256957,0.017462948328216068,0.6607572115384616 | |
| 48 | −house,municipality,11,3328,0.024346670361835077,-0.00006186481278868769,0.025607525987373706,-0.0005609351745473852,0.016998055871441813,0.017679407791658816,0.6613581730769231 | |
| 49 | −house,municipality,12,3328,0.02425204686382497,-0.00009108103528319962,0.025347260810956472,-0.00041864634387106785,0.016786336051117897,0.017276264088198927,0.6664663461538461 | |
| 26 | +condo,region,1,832,0.004815282942116754,-0.0002048664065512237,0.01225082111850245,-0.0017439461008006913,0.004815282942116754,0.012250821118502443,0.11899038461538461 | |
| 27 | +condo,region,2,832,0.004347833425639186,-0.000054928397888107526,0.010715152514655804,-0.0017418186855441904,0.004347833425639184,0.010715152514655803,0.08533653846153846 | |
| 28 | +condo,region,3,832,0.004267731748830362,-0.00011285894486369165,0.010285931639802064,-0.0020162287732991453,0.004267731748830359,0.010285931639802058,0.0889423076923077 | |
| 29 | +condo,region,4,832,0.004368101723317243,-0.00014416239404632712,0.009942727563940117,-0.002212261846238497,0.004368101723317243,0.009942727563940112,0.09495192307692307 | |
| 30 | +condo,region,5,832,0.004256282842940073,-0.0001034763180220804,0.009972361173465148,-0.00230200187317801,0.004256282842940073,0.009972361173465144,0.08533653846153846 | |
| 31 | +condo,region,6,832,0.004295517236291511,0.000046813718108922045,0.010024479064542492,-0.002470560797695763,0.004295517236291512,0.010024479064542489,0.09014423076923077 | |
| 32 | +condo,region,7,832,0.004246459573725979,0.000013730353814239167,0.010083870970401334,-0.0027299052940435217,0.004246459573725979,0.01008387097040133,0.08653846153846154 | |
| 33 | +condo,region,8,832,0.004171883745800042,-0.00010249367820354731,0.009828586226345682,-0.0025446153773471705,0.004171883745800042,0.00982858622634568,0.08653846153846154 | |
| 34 | +condo,region,9,832,0.004138445837807288,-0.0002577106168852698,0.009649299951990117,-0.0025298707125616143,0.004138445837807288,0.009649299951990115,0.08413461538461539 | |
| 35 | +condo,region,10,832,0.004058523981962813,-0.0002876612293999924,0.009400236519405893,-0.002332499224315757,0.004058523981962814,0.009400236519405893,0.07692307692307693 | |
| 36 | +condo,region,11,832,0.0040160759640281065,-0.0002782424877234322,0.009095214846462125,-0.0022215002986432685,0.004016075964028107,0.009095214846462122,0.06850961538461539 | |
| 37 | +condo,region,12,832,0.003947089405672689,-0.0002580896991401802,0.008802795015332876,-0.001934980306822396,0.00394708940567269,0.008802795015332874,0.07211538461538461 | |
| 38 | +house,municipality,1,3328,0.017207803555966312,-0.00030668003381844266,0.020922047594819807,-0.0013453911660756509,0.017257516102550004,0.021197564713390665,0.5441706730769231 | |
| 39 | +house,municipality,2,3328,0.01612939720615663,0.00011056731673852126,0.01826370877878368,-0.0008972328581619059,0.015822460609235485,0.017910683670666925,0.5033052884615384 | |
| 40 | +house,municipality,3,3328,0.01591168203831903,-0.0002570670664799,0.01775757791296206,-0.0011228921263207539,0.015567897480407045,0.017251405961658202,0.47866586538461536 | |
| 41 | +house,municipality,4,3328,0.0156071961538179,-0.00006682445461429427,0.017373963026773173,-0.001079314893204081,0.01520157817137899,0.016854543933627255,0.4753605769230769 | |
| 42 | +house,municipality,5,3328,0.015519985796975826,-0.0002373421571513687,0.01738142069665267,-0.0009954226688078925,0.015079034953772615,0.016903604136266417,0.47055288461538464 | |
| 43 | +house,municipality,6,3328,0.015514368042232128,0.00013838804838437743,0.01754021416023113,-0.001187186406815885,0.015081878769834942,0.017053019536078153,0.4621394230769231 | |
| 44 | +house,municipality,7,3328,0.01564223644535282,-0.00016646132761997755,0.0176393105078625,-0.00136562025425915,0.015197467282081066,0.017087610414720907,0.46274038461538464 | |
| 45 | +house,municipality,8,3328,0.01579588099304743,-5.511477019134148e-7,0.01766082737930761,-0.0012709840041161726,0.015314526910658488,0.01712484760327838,0.4651442307692308 | |
| 46 | +house,municipality,9,3328,0.015930228217126315,-0.0002502637857055798,0.017407731338920915,-0.001436471710543427,0.015429618555887176,0.016885756770712544,0.4729567307692308 | |
| 47 | +house,municipality,10,3328,0.016090395784124956,-0.00014198672242951246,0.017174599314093663,-0.0012231830759746713,0.015579018702746115,0.016640860401679083,0.47325721153846156 | |
| 48 | +house,municipality,11,3328,0.0163602412782793,-0.00007022873835416781,0.01730053144214932,-0.0009931870622279465,0.015866267852065914,0.016772690305529924,0.48106971153846156 | |
| 49 | +house,municipality,12,3328,0.016212554961847207,-0.00009694109732339254,0.01700110926135903,-0.000891863481859751,0.015697989317851144,0.016384297452680673,0.4777644230769231 | |
| 50 | 50 | house,province,1,52,0.0015532707391587841,-0.00012648911680931643,0.0012677792394390134,-0.0006682393886121419,0.0015532707391587958,0.0012677792394390347,0.0 |
| 51 | 51 | house,province,2,52,0.0016559392160083,-0.00008235723045537411,0.0013362779831777101,-0.0007866040235986188,0.0016559392160083047,0.0013362779831777225,0.0 |
| 52 | 52 | house,province,3,52,0.001583892533307463,-0.000043611870685160214,0.001412174589921807,-0.0008420760005469064,0.0015838925333074663,0.0014121745899218123,0.0 |
@@ -59,30 +59,30 @@ house,province,9,52,0.0014130734327221692,-0.0000909970886723188,0.0014631138909 | ||
| 59 | 59 | house,province,10,52,0.0014782505022436498,-0.00007538162157755709,0.0015200536868807373,-0.0010272498521163638,0.0014782505022436407,0.0015200536868807328,0.0 |
| 60 | 60 | house,province,11,52,0.0015230332258875645,-2.250496928529023e-6,0.0014643526037635517,-0.0010100131367339448,0.0015230332258875609,0.0014643526037635562,0.0 |
| 61 | 61 | house,province,12,52,0.0015153200471414187,-0.00010782398525555524,0.0014670531390204471,-0.0009616150650127447,0.0015153200471414168,0.0014670531390204601,0.0 |
| 62 | −house,region,1,884,0.019341166648931353,0.00013914401138195157,0.022435845036222188,-0.000054689108704933114,0.01783500175212258,0.021391270357664364,0.5305429864253394 | |
| 63 | −house,region,2,884,0.019461798425585455,-0.000020573850304829773,0.02195862296461838,-0.0001408973103168795,0.017848468990383434,0.020992692267059982,0.5271493212669683 | |
| 64 | −house,region,3,884,0.019640510488117734,0.0004008813035882496,0.022243928533564016,0.000037173789520607527,0.018068003224688576,0.021319531400553697,0.5373303167420814 | |
| 65 | −house,region,4,884,0.019351892777547952,0.00035417419660580137,0.02278751020208834,-0.0007848639223575325,0.017899705511420682,0.02177258496806936,0.5237556561085973 | |
| 66 | −house,region,5,884,0.019091097073210165,-0.00018818138959991127,0.022931771132624845,-0.0011419958694885563,0.017653390438943522,0.02165714183348216,0.5248868778280543 | |
| 67 | −house,region,6,884,0.019139962082195424,-0.00048142272931152904,0.023312582951215185,-0.0015064362333008757,0.017726656446890955,0.02189243380022784,0.5294117647058824 | |
| 68 | −house,region,7,884,0.01904469005779749,-0.0003719533571034606,0.02328476245583892,-0.000838248894445598,0.017692722186945373,0.021746034209333277,0.5418552036199095 | |
| 69 | −house,region,8,884,0.019261939850554546,-0.00026944596776266893,0.02329584417620687,-0.0004888714063228724,0.018005169160605173,0.02155822569409291,0.5373303167420814 | |
| 70 | −house,region,9,884,0.019267639722951157,-0.00003037649320443736,0.023305620403128734,-0.00035999914495570917,0.018186075121703563,0.021486607965907668,0.5350678733031674 | |
| 71 | −house,region,10,884,0.01914122368946062,0.00010720487911149635,0.023078835788251314,-0.00010840283347579008,0.01840151268136635,0.021350152425829293,0.5260180995475113 | |
| 72 | −house,region,11,884,0.01946217375677165,0.00017326483991043616,0.02332239123772778,0.00012918004126570213,0.01877205917976002,0.02168636582193896,0.5158371040723982 | |
| 73 | −house,region,12,884,0.019405316236140027,0.0001325954479839686,0.022926702846803357,0.00020440160720895,0.01880405105246462,0.02125108347601303,0.5090497737556561 | |
| 74 | −plex,municipality,1,3328,0.030288452162634486,-0.0005165186498657069,0.05262428846302046,-0.0013226885566379341,0.019897252981619543,0.033129705017398134,0.7349759615384616 | |
| 75 | −plex,municipality,2,3328,0.030266879188481352,-0.0003655847747851418,0.05124090476264386,-0.0008382047477597895,0.01989114441298446,0.032497132965224954,0.7409855769230769 | |
| 76 | −plex,municipality,3,3328,0.030331666170240246,-0.0002023008851125926,0.050822375628742804,-0.0013142855616835884,0.020014972187375853,0.032151556987150706,0.7400841346153846 | |
| 77 | −plex,municipality,4,3328,0.030477212650632968,0.00009434774566381735,0.05035652503326435,-0.001269901688993596,0.019986435679324968,0.031484862709542816,0.7484975961538461 | |
| 78 | −plex,municipality,5,3328,0.030384353808983886,-0.0002506971999339553,0.050557107407382555,-0.0015328881303532374,0.019852268026197755,0.03133718273027722,0.7563100961538461 | |
| 79 | −plex,municipality,6,3328,0.03047240597019304,-0.0006416287762726019,0.050801391274887295,-0.0012769798940341077,0.019520257572899293,0.03113832132401664,0.7478966346153846 | |
| 80 | −plex,municipality,7,3328,0.030097634848138103,0.0005177780192227456,0.05285719878650923,-0.00004594055955131114,0.019349417672062698,0.03183955846264396,0.7424879807692307 | |
| 81 | −plex,municipality,8,3328,0.029959285314090908,0.00012593807399265388,0.05247757118949334,-0.0007128007364661556,0.019118259744491658,0.03136403272927994,0.73828125 | |
| 82 | −plex,municipality,9,3328,0.030089488148415975,0.00045823265598124614,0.052224433356263526,-0.0009431994798718081,0.01901532501819489,0.03092802726606761,0.7391826923076923 | |
| 83 | −plex,municipality,10,3328,0.030194143109486052,-0.0003324314840843651,0.05221499953743071,-0.00144042255688976,0.019153754370377817,0.03092370755108541,0.7430889423076923 | |
| 84 | −plex,municipality,11,3328,0.030196699685274782,-0.0005071502270420179,0.05194727249336939,-0.0009267387182001881,0.019627275902071434,0.03058956960222138,0.7409855769230769 | |
| 85 | −plex,municipality,12,3328,0.030019016362902717,0.0002973269455979839,0.05150005703267959,-9.476544628591346e-6,0.019092770944119066,0.029870112776456893,0.7418870192307693 | |
| 62 | +house,region,1,884,0.016237242156680445,0.00009591728661120929,0.02160711299012935,0.00003595372178457736,0.01623724215668044,0.021607112990129344,0.43778280542986425 | |
| 63 | +house,region,2,884,0.015642093605174212,-0.000011515089719601417,0.019929006720601935,0.00006297926149870425,0.015642093605174205,0.01992900672060193,0.4117647058823529 | |
| 64 | +house,region,3,884,0.015653394118746635,0.00022559115679057418,0.019759598660642492,0.00019333471335641601,0.01565339411874663,0.019759598660642485,0.3993212669683258 | |
| 65 | +house,region,4,884,0.015383705603804093,0.0002478981369101258,0.020166006796198382,-0.00032851871674480465,0.015383705603804088,0.02016600679619837,0.3891402714932127 | |
| 66 | +house,region,5,884,0.015082412216639045,-0.00008129335178385775,0.019945240542811524,-0.0005223528424247025,0.015082412216639042,0.01994524054281152,0.3766968325791855 | |
| 67 | +house,region,6,884,0.01512963395979052,-0.00029379190012712513,0.019966638802144342,-0.0008302445651651559,0.015129633959790516,0.01996663880214434,0.36538461538461536 | |
| 68 | +house,region,7,884,0.014970837398918435,-0.0002886369206104624,0.019902198359729287,-0.0004611578016714547,0.01497083739891843,0.019902198359729287,0.3744343891402715 | |
| 69 | +house,region,8,884,0.01518370251745822,-0.00019827012222823565,0.01977226195259539,-0.00009632109602087075,0.015183702517458215,0.019772261952595387,0.3778280542986425 | |
| 70 | +house,region,9,884,0.015304326289318883,0.000044315382091241464,0.01972433776341646,0.00008674979855672094,0.015304326289318878,0.019724337763416457,0.3891402714932127 | |
| 71 | +house,region,10,884,0.0154662237603004,0.00011144927632603617,0.019665615993345088,0.0004705862421928911,0.015466223760300397,0.019665615993345084,0.40158371040723984 | |
| 72 | +house,region,11,884,0.01576304210311467,0.00016789949884931616,0.019966236719969763,0.0007366686831810183,0.015763042103114668,0.01996623671996976,0.3993212669683258 | |
| 73 | +house,region,12,884,0.01587220245953274,0.00001639036867535654,0.019572158747650874,0.0008012208822167359,0.015872202459532737,0.019572158747650868,0.40384615384615385 | |
| 74 | +plex,municipality,1,3328,0.015819712039832116,0.00013506117106964227,0.03949344488472662,-0.002438728817984168,0.015661197364926237,0.03972903250446488,0.4840745192307692 | |
| 75 | +plex,municipality,2,3328,0.014155347906987555,-0.00020780622715853216,0.03467918451575128,-0.0025472181463809247,0.013917599734152962,0.03426072291577735,0.4284855769230769 | |
| 76 | +plex,municipality,3,3328,0.01352089419910673,-0.00019994096084072603,0.032198233488122535,-0.0028867438834466515,0.013159666509577821,0.0314106491803168,0.39903846153846156 | |
| 77 | +plex,municipality,4,3328,0.013373559497127001,-0.00019363965502364191,0.029986664146197384,-0.002611423968690828,0.012962374261267201,0.028885611975837482,0.3996394230769231 | |
| 78 | +plex,municipality,5,3328,0.013092261545797238,-0.0003083250545240422,0.02888613327078242,-0.0025596989456945466,0.012661761172431868,0.027628531968372526,0.38852163461538464 | |
| 79 | +plex,municipality,6,3328,0.012931887606002567,-0.0005699946661446375,0.027952593521416356,-0.002449588276123388,0.012554899833009349,0.02667866099583461,0.37439903846153844 | |
| 80 | +plex,municipality,7,3328,0.012810605655911607,0.000289078037017901,0.027604097610961643,-0.002044558755162899,0.012368925396553236,0.026353625658609173,0.3746995192307692 | |
| 81 | +plex,municipality,8,3328,0.01270669758817127,-0.00015771912833731304,0.026919362943800457,-0.002550672174675075,0.012297636219625948,0.025632186974514824,0.3786057692307692 | |
| 82 | +plex,municipality,9,3328,0.012597955586376327,-0.0001711301482021033,0.02627871973270691,-0.00280139354266408,0.012191011358850099,0.02487804471992416,0.37349759615384615 | |
| 83 | +plex,municipality,10,3328,0.012666605394109628,-0.0005531121887786219,0.025662974185262907,-0.0030838578405113543,0.01226355349484181,0.024291590276601095,0.3798076923076923 | |
| 84 | +plex,municipality,11,3328,0.01291053792527181,-0.0004999504987514736,0.025029084534822794,-0.002801129668997038,0.012547223083908167,0.023539957540193238,0.3831129807692308 | |
| 85 | +plex,municipality,12,3328,0.0127042317698907,-1.2494983106196225e-6,0.02466328268627723,-0.002297135212747703,0.012323305400044159,0.023067116526959308,0.38070913461538464 | |
| 86 | 86 | plex,province,1,52,0.0025609731874916717,-0.00013590920564457217,0.0034534763426082977,-0.0015875438365012494,0.0025609731874916665,0.0034534763426082946,0.0 |
| 87 | 87 | plex,province,2,52,0.0024926043659790893,-0.00009454019214262746,0.0032054734636291324,-0.0014694410088973257,0.0024926043659790685,0.003205473463629128,0.019230769230769232 |
| 88 | 88 | plex,province,3,52,0.002553714357481464,-0.00004047794281379422,0.003094585608310371,-0.0015220777776656772,0.002553714357481438,0.003094585608310366,0.0 |
@@ -95,15 +95,15 @@ plex,province,9,52,0.0025205385749526854,-0.00011984928174292413,0.0026124175074 | ||
| 95 | 95 | plex,province,10,52,0.002471275260724911,-0.00016212659033303835,0.0025179190218171587,-0.0009736164485255596,0.0024712752607249015,0.002517919021817157,0.0 |
| 96 | 96 | plex,province,11,52,0.0023786308212119735,0.00008397121580975361,0.002409360630624702,-0.0007613081176061588,0.0023786308212119666,0.002409360630624712,0.0 |
| 97 | 97 | plex,province,12,52,0.002372654388962432,-0.00017528002044779283,0.0025522750376679062,-0.0008278294224610996,0.0023726543889624327,0.0025522750376679223,0.0 |
| 98 | −plex,region,1,884,0.013662550268308716,-0.0003106828386291632,0.020509090851500367,-0.004066840589696208,0.008588373597085176,0.013735934909154963,0.5328054298642534 | |
| 99 | −plex,region,2,884,0.01380938733905172,-0.00005507667807821284,0.020321558543735557,-0.004052497859157451,0.008743558386230163,0.013775239910577506,0.5395927601809954 | |
| 100 | −plex,region,3,884,0.013579203115088274,-0.00015589132251798307,0.02036969763369768,-0.004311733216030613,0.008691208480082676,0.013924305443190406,0.5248868778280543 | |
| 101 | −plex,region,4,884,0.013639439516273274,-0.00021329719406405447,0.020611391769852692,-0.0042585236155864474,0.008751234298494903,0.013840468072287814,0.5271493212669683 | |
| 102 | −plex,region,5,884,0.01382307529096038,-0.00031560954542261154,0.021284503765627488,-0.004218441495675318,0.008739481647510192,0.0142695085422924,0.5328054298642534 | |
| 103 | −plex,region,6,884,0.013543587939921909,-0.0007488494780056365,0.021803628079206022,-0.004280267650932594,0.008605126816542623,0.014021650843498165,0.5316742081447964 | |
| 104 | −plex,region,7,884,0.013396622009093124,-0.0005177203933074815,0.022461321433010415,-0.004277202804897456,0.008728996501200475,0.014374504850619765,0.5226244343891403 | |
| 105 | −plex,region,8,884,0.013425408159991752,-0.00042760211871508845,0.02204763031635865,-0.004091255775097806,0.00864160129784591,0.014318956170876553,0.5237556561085973 | |
| 106 | −plex,region,9,884,0.013700099938324965,-0.0002571476814299578,0.02202374907087782,-0.004011719013280068,0.008795275562324676,0.014197163527242035,0.5328054298642534 | |
| 107 | −plex,region,10,884,0.013954066485638647,-0.00038978431401202163,0.02195315127310212,-0.0037817512916404287,0.008956589306037753,0.014001053319166847,0.5452488687782805 | |
| 108 | −plex,region,11,884,0.014203977274158053,-0.00022764982825614356,0.02185184168913049,-0.0033312202054388597,0.009293019546997147,0.014028442873695878,0.5599547511312217 | |
| 109 | −plex,region,12,884,0.014069327265242619,-0.0004475291972578539,0.021825252254585842,-0.0032864627859346017,0.009161575036737011,0.014281809023605587,0.5497737556561086 | |
| 98 | +plex,region,1,884,0.0076052365805900505,-0.00023694798124015116,0.015527748636949855,-0.00212428904141148,0.007605236580590048,0.015527748636949855,0.25904977375565613 | |
| 99 | +plex,region,2,884,0.006447377736207441,-0.00008402580505494034,0.012584425501159945,-0.0020182324584237022,0.006447377736207441,0.012584425501159945,0.19683257918552036 | |
| 100 | +plex,region,3,884,0.005870393093142987,-0.00014677742464745284,0.01157925570090931,-0.0022065035744488143,0.005870393093142987,0.011579255700909306,0.1504524886877828 | |
| 101 | +plex,region,4,884,0.005862754623190244,-0.00019418627565274293,0.011346699078081753,-0.002112436792889703,0.005862754623190244,0.011346699078081746,0.15610859728506787 | |
| 102 | +plex,region,5,884,0.005729513848660099,-0.000247923096635582,0.011456625339177032,-0.0020211916862878337,0.005729513848660103,0.011456625339177027,0.14705882352941177 | |
| 103 | +plex,region,6,884,0.005658962446315207,-0.0003010322368330697,0.011187565160104159,-0.0019449400400900377,0.005658962446315207,0.011187565160104156,0.13914027149321267 | |
| 104 | +plex,region,7,884,0.005717764103085898,-0.00010850895088784576,0.011086865793034152,-0.0019979554117634814,0.005717764103085898,0.011086865793034145,0.1425339366515837 | |
| 105 | +plex,region,8,884,0.005674841401389892,-0.00009026569973010005,0.011001736886019443,-0.002033380275588575,0.005674841401389891,0.011001736886019438,0.1414027149321267 | |
| 106 | +plex,region,9,884,0.005749499162117269,-0.00008960245046673149,0.011027784437214683,-0.002082687542358572,0.0057494991621172675,0.011027784437214682,0.1436651583710407 | |
| 107 | +plex,region,10,884,0.005927436908831604,-0.00024841794076044954,0.010993869820774826,-0.002011109260497193,0.0059274369088316,0.01099386982077482,0.15497737556561086 | |
| 108 | +plex,region,11,884,0.006170490922417883,-0.00012127384691599895,0.011028632569487593,-0.0017871849758839895,0.006170490922417881,0.01102863256948759,0.16063348416289594 | |
| 109 | +plex,region,12,884,0.006110026845964132,-0.00025532733258641497,0.011280212739165813,-0.001892255480184082,0.006110026845964128,0.011280212739165815,0.16063348416289594 | |
added
results/vintages/revisions_by_release_filtered.csv
+109 −0
@@ -0,0 +1,109 @@ | ||
| 1 | +class,level,release,obs,mae_rev_m1_vs_final,bias_rev_m1_vs_final,mae_rev_12m_vs_final,bias_rev_12m_vs_final,mae_rev_m1_vs_last_rt,mae_rev_12m_vs_last_rt,share_m1_rev_gt_1pct | |
| 2 | +condo,municipality,1,3326,0.018218959210363525,0.000016688468351379933,0.035899729145987345,-0.00021023850922487382,0.010687310523249133,0.018543645860963492,0.539386650631389 | |
| 3 | +condo,municipality,2,3326,0.01830369137665762,0.0003420352663881489,0.035203232880077505,0.000014803896699086178,0.010651332141485633,0.01800571985494987,0.5426939266386049 | |
| 4 | +condo,municipality,3,3326,0.018253256323750244,0.0001697960142438505,0.03491731551544456,-0.00007206650223968792,0.010565016295267286,0.017769186486300787,0.5469031870114252 | |
| 5 | +condo,municipality,4,3326,0.018498001562029184,-0.0002326810118876801,0.03468796870069724,-0.0000658947904044443,0.010597045308313584,0.017713744598356427,0.5463018641010222 | |
| 6 | +condo,municipality,5,3326,0.01890243926725158,0.000215819868632482,0.035178307015601325,-0.00016159941438881665,0.010842865379196506,0.017952100712717328,0.550210463018641 | |
| 7 | +condo,municipality,6,3326,0.01881462255308852,0.0002875399574424479,0.03532503607865267,-0.0007016909438070628,0.01091535931184554,0.018263884216127575,0.5493084786530367 | |
| 8 | +condo,municipality,7,3326,0.018671778319748422,0.00033286267475003597,0.036041158805345176,-0.0008354496235605418,0.010687919474979822,0.01848474169474526,0.5472038484666266 | |
| 9 | +condo,municipality,8,3326,0.01853143947049999,-0.00016892757131686565,0.03586994538523937,-0.000523705132569303,0.010673162772855687,0.01829841904002192,0.544798556825015 | |
| 10 | +condo,municipality,9,3326,0.01858009111707426,-0.00034492971547748645,0.03545922010754932,-0.00018396812920626682,0.010507553779923871,0.017667555346076835,0.5514131088394468 | |
| 11 | +condo,municipality,10,3326,0.0185982825211036,-0.0003207810791853325,0.034781251007409114,0.0003476424187927076,0.01036657598267548,0.017125414330917934,0.5472038484666266 | |
| 12 | +condo,municipality,11,3326,0.018560632418666984,-0.0004204284203259435,0.034129410672541334,0.0011552891842648075,0.010121351342934837,0.016301168671367203,0.5532170775706554 | |
| 13 | +condo,municipality,12,3326,0.018713291616592024,-0.00008723296083991091,0.033784122050478343,0.0018469170231783935,0.010085362730969969,0.016165427891360936,0.5544197233914612 | |
| 14 | +condo,province,1,52,0.0015620242064084064,0.00012999474800442522,0.0024936086388600643,0.002180781249067779,0.0015620242064083995,0.0024936086388600443,0.0 | |
| 15 | +condo,province,2,52,0.001554788940591851,0.00021421155617977366,0.0024556995539281204,0.0021945097556216576,0.0015547889405918415,0.002455699553928104,0.0 | |
| 16 | +condo,province,3,52,0.0016383411282475647,0.00007172615245495256,0.002385968263163059,0.002127974502410657,0.00163834112824756,0.0023859682631630474,0.0 | |
| 17 | +condo,province,4,52,0.0016851200317672967,0.00015760816287830706,0.002420257628965046,0.0021891023199823692,0.001685120031767297,0.0024202576289650305,0.0 | |
| 18 | +condo,province,5,52,0.0016060504415260231,0.0001648223290224312,0.0023661487983808183,0.002172771548220345,0.0016060504415260288,0.002366148798380805,0.0 | |
| 19 | +condo,province,6,52,0.0016567502022934756,0.00028747540064996235,0.002405214592967795,0.0022068356583684673,0.0016567502022934812,0.00240521459296778,0.0 | |
| 20 | +condo,province,7,52,0.0016786515058316567,0.00020647669810997852,0.0023240913353446534,0.0020709014067529876,0.0016786515058316586,0.002324091335344652,0.0 | |
| 21 | +condo,province,8,52,0.001655389863398211,0.00024328274921799384,0.0022743724214897825,0.0020328784287352046,0.0016553898633982099,0.002274372421489772,0.0 | |
| 22 | +condo,province,9,52,0.001684486401976774,0.0001661810791386928,0.0021740109318927232,0.0018353554819828383,0.0016844864019767622,0.002174010931892708,0.0 | |
| 23 | +condo,province,10,52,0.001593885852531655,0.00016859658055825427,0.002117012954797542,0.0018047875724924965,0.0015938858525316494,0.0021170129547975255,0.0 | |
| 24 | +condo,province,11,52,0.0016030353170648113,0.00016609797181298672,0.001982236262739768,0.0017272436940498302,0.0016030353170648011,0.001982236262739752,0.0 | |
| 25 | +condo,province,12,52,0.0015979690610675432,0.00020430782104002075,0.002030825256681998,0.0017622010900594173,0.0015979690610675526,0.002030825256681985,0.0 | |
| 26 | +condo,region,1,832,0.009790266447409655,-0.0000311044809995112,0.019273842101740615,-0.003434750106696979,0.006077331017071481,0.011545513715423313,0.34134615384615385 | |
| 27 | +condo,region,2,832,0.009842418822069127,-0.00005817544655845742,0.01915099154614561,-0.0037935825205749115,0.005901925322208564,0.011257364932724868,0.3401442307692308 | |
| 28 | +condo,region,3,832,0.009979287810595174,-0.0002349253918682097,0.01927819307081639,-0.004153633182955936,0.006011492519209637,0.011521417882571442,0.34134615384615385 | |
| 29 | +condo,region,4,832,0.010126823709202846,-0.0003339756696797231,0.019392051992608312,-0.004460492510153581,0.006075055890613615,0.01172819635641147,0.35216346153846156 | |
| 30 | +condo,region,5,832,0.010179366354186963,-0.00028659790632558336,0.019793882996838732,-0.0047910316429116825,0.006048436736559418,0.012013468400404903,0.36177884615384615 | |
| 31 | +condo,region,6,832,0.010367298598993564,-0.00022179320681323326,0.02002685451109216,-0.005158897833503317,0.006257140672470927,0.012170491393923113,0.3701923076923077 | |
| 32 | +condo,region,7,832,0.010192976572009942,-0.00012872223556806715,0.020767326355680602,-0.004593290801631859,0.006123962972048171,0.012422729646467559,0.36538461538461536 | |
| 33 | +condo,region,8,832,0.01020605005605612,-0.00042945004374352205,0.020638108116871796,-0.004459793318129071,0.006134809678767996,0.012179908602905019,0.35216346153846156 | |
| 34 | +condo,region,9,832,0.010106086957893909,-0.0004738868148322336,0.02058157780165701,-0.004521995638758401,0.006081644606522165,0.011882547908836187,0.34134615384615385 | |
| 35 | +condo,region,10,832,0.01008205418619355,-0.0003357188015692197,0.02034217407559042,-0.004484759347404402,0.005893884835961093,0.011589247146868181,0.34375 | |
| 36 | +condo,region,11,832,0.00999206557050614,-0.00041264004127453417,0.020295766446709166,-0.004603531125417201,0.0058148474635401105,0.011335083931070078,0.3401442307692308 | |
| 37 | +condo,region,12,832,0.009847622079466455,-0.0004877600674646843,0.02003105454023685,-0.004426418932769244,0.005679289338834248,0.01112211780606469,0.33413461538461536 | |
| 38 | +house,municipality,1,3328,0.023092188875919885,-0.00010638684910900398,0.024208956837254495,-0.0009847268772388566,0.016119970404192843,0.017647401865411975,0.6538461538461539 | |
| 39 | +house,municipality,2,3328,0.02305826378044436,0.00007766180718067762,0.024121334462707294,-0.0007335340189827424,0.016076172463857133,0.01754601518602517,0.6607572115384616 | |
| 40 | +house,municipality,3,3328,0.023314129313209635,-0.00018437102168174889,0.0243558017900071,-0.0008055282238277189,0.016161186990447114,0.017631539281284643,0.6574519230769231 | |
| 41 | +house,municipality,4,3328,0.023094714275644447,-0.000012429057348933915,0.024438727072204902,-0.000745014844955078,0.015984483975738917,0.017493185039469124,0.6487379807692307 | |
| 42 | +house,municipality,5,3328,0.02319062035455638,-0.0002150897436110274,0.024551891295953435,-0.0008033047850721948,0.016033372472342513,0.017595766690442386,0.6544471153846154 | |
| 43 | +house,municipality,6,3328,0.023469611454049084,0.00006959664267645536,0.024778345097935032,-0.0008921591898266257,0.016046905816510533,0.0178106438497968,0.6556490384615384 | |
| 44 | +house,municipality,7,3328,0.023757462631077236,-0.00008859729964317436,0.02580790419931541,-0.001064482240453408,0.01616931963135506,0.017893589258424866,0.6526442307692307 | |
| 45 | +house,municipality,8,3328,0.02405027377329403,0.000036784759826624524,0.025928003607596735,-0.0009488613149150439,0.016375509629565892,0.017865497478796667,0.6589543269230769 | |
| 46 | +house,municipality,9,3328,0.024112394613001943,-0.00035144119494706926,0.025740121764867226,-0.0008296229136360708,0.016516847008696097,0.017721469248294538,0.6613581730769231 | |
| 47 | +house,municipality,10,3328,0.02415853926255957,-0.00005750907250976943,0.025534692908016896,-0.0006307256274651758,0.01667436399825696,0.017462948328216068,0.6607572115384616 | |
| 48 | +house,municipality,11,3328,0.024346670361835077,-0.00006186481278868747,0.025607525987373706,-0.0005609351745473847,0.016998055871441813,0.017679407791658816,0.6613581730769231 | |
| 49 | +house,municipality,12,3328,0.02425204686382497,-0.00009108103528319929,0.025347260810956472,-0.00041864634387106764,0.016786336051117897,0.017276264088198927,0.6664663461538461 | |
| 50 | +house,province,1,52,0.0015532707391587841,-0.00012648911680931643,0.0012677792394390134,-0.0006682393886121419,0.0015532707391587958,0.0012677792394390347,0.0 | |
| 51 | +house,province,2,52,0.0016559392160083,-0.00008235723045537411,0.0013362779831777101,-0.0007866040235986188,0.0016559392160083047,0.0013362779831777225,0.0 | |
| 52 | +house,province,3,52,0.001583892533307463,-0.000043611870685160214,0.001412174589921807,-0.0008420760005469064,0.0015838925333074663,0.0014121745899218123,0.0 | |
| 53 | +house,province,4,52,0.0015542758198942297,-0.00009829276850291391,0.0015479190215160433,-0.001004210665083142,0.0015542758198942381,0.001547919021516063,0.0 | |
| 54 | +house,province,5,52,0.0014986613545432267,-0.00004873313943422304,0.0015508090247639411,-0.000989535425208228,0.0014986613545432334,0.0015508090247639593,0.0 | |
| 55 | +house,province,6,52,0.0015343173963489629,0.00006115591403258357,0.0015003670779485277,-0.0010266957469961696,0.0015343173963489594,0.0015003670779485405,0.0 | |
| 56 | +house,province,7,52,0.0015909958050343209,-0.0000190056585240687,0.0015965742541963228,-0.0011773220699620114,0.0015909958050343202,0.001596574254196325,0.0 | |
| 57 | +house,province,8,52,0.001452542351743374,-0.000034452325799709044,0.0015281850812553575,-0.0011388022641749306,0.0014525423517433717,0.0015281850812553608,0.0 | |
| 58 | +house,province,9,52,0.0014130734327221692,-0.0000909970886723188,0.0014631138909238816,-0.0011195581751700361,0.0014130734327221666,0.0014631138909238816,0.0 | |
| 59 | +house,province,10,52,0.0014782505022436498,-0.00007538162157755709,0.0015200536868807373,-0.0010272498521163638,0.0014782505022436407,0.0015200536868807328,0.0 | |
| 60 | +house,province,11,52,0.0015230332258875645,-2.250496928529023e-6,0.0014643526037635517,-0.0010100131367339448,0.0015230332258875609,0.0014643526037635562,0.0 | |
| 61 | +house,province,12,52,0.0015153200471414187,-0.00010782398525555524,0.0014670531390204471,-0.0009616150650127447,0.0015153200471414168,0.0014670531390204601,0.0 | |
| 62 | +house,region,1,884,0.019341166648931353,0.00013914401138195105,0.022435845036222188,-0.00005468910870493333,0.01783500175212258,0.02139127035766436,0.5305429864253394 | |
| 63 | +house,region,2,884,0.019461798425585455,-0.000020573850304829553,0.02195862296461838,-0.00014089731031687887,0.017848468990383434,0.02099269226705998,0.5271493212669683 | |
| 64 | +house,region,3,884,0.019640510488117734,0.00040088130358824966,0.022243928533564016,0.00003717378952060734,0.018068003224688576,0.021319531400553697,0.5373303167420814 | |
| 65 | +house,region,4,884,0.019351892777547952,0.00035417419660580137,0.02278751020208834,-0.0007848639223575326,0.017899705511420682,0.02177258496806936,0.5237556561085973 | |
| 66 | +house,region,5,884,0.019091097073210165,-0.0001881813895999114,0.022931771132624845,-0.001141995869488556,0.017653390438943522,0.021657141833482163,0.5248868778280543 | |
| 67 | +house,region,6,884,0.01913996208219542,-0.0004814227293115287,0.023312582951215185,-0.0015064362333008757,0.017726656446890955,0.02189243380022784,0.5294117647058824 | |
| 68 | +house,region,7,884,0.019044690057797495,-0.0003719533571034608,0.02328476245583892,-0.0008382488944455985,0.017692722186945373,0.021746034209333277,0.5418552036199095 | |
| 69 | +house,region,8,884,0.019261939850554546,-0.0002694459677626691,0.02329584417620687,-0.0004888714063228725,0.018005169160605173,0.02155822569409291,0.5373303167420814 | |
| 70 | +house,region,9,884,0.019267639722951157,-0.000030376493204437013,0.023305620403128734,-0.00035999914495570863,0.018186075121703563,0.021486607965907668,0.5350678733031674 | |
| 71 | +house,region,10,884,0.01914122368946062,0.00010720487911149595,0.023078835788251314,-0.00010840283347579039,0.01840151268136635,0.02135015242582929,0.5260180995475113 | |
| 72 | +house,region,11,884,0.01946217375677165,0.00017326483991043657,0.02332239123772778,0.000129180041265702,0.01877205917976002,0.02168636582193896,0.5158371040723982 | |
| 73 | +house,region,12,884,0.019405316236140027,0.00013259544798396858,0.022926702846803357,0.00020440160720895014,0.01880405105246462,0.02125108347601303,0.5090497737556561 | |
| 74 | +plex,municipality,1,3328,0.030288452162634486,-0.0005165186498657076,0.05262428846302046,-0.0013226885566379341,0.019897252981619543,0.033129705017398134,0.7349759615384616 | |
| 75 | +plex,municipality,2,3328,0.030266879188481352,-0.00036558477478514133,0.05124090476264386,-0.0008382047477597897,0.01989114441298446,0.032497132965224954,0.7409855769230769 | |
| 76 | +plex,municipality,3,3328,0.030331666170240246,-0.000202300885112592,0.050822375628742804,-0.0013142855616835889,0.020014972187375853,0.032151556987150706,0.7400841346153846 | |
| 77 | +plex,municipality,4,3328,0.030477212650632968,0.00009434774566381689,0.05035652503326435,-0.0012699016889935971,0.019986435679324968,0.031484862709542816,0.7484975961538461 | |
| 78 | +plex,municipality,5,3328,0.03038435380898388,-0.00025069719993395413,0.050557107407382555,-0.0015328881303532371,0.01985226802619775,0.03133718273027722,0.7563100961538461 | |
| 79 | +plex,municipality,6,3328,0.03047240597019304,-0.000641628776272603,0.050801391274887295,-0.0012769798940341086,0.019520257572899293,0.03113832132401664,0.7478966346153846 | |
| 80 | +plex,municipality,7,3328,0.030097634848138103,0.0005177780192227458,0.05285719878650923,-0.00004594055955131107,0.019349417672062698,0.03183955846264396,0.7424879807692307 | |
| 81 | +plex,municipality,8,3328,0.029959285314090908,0.00012593807399265434,0.05247757118949334,-0.0007128007364661554,0.019118259744491658,0.03136403272927994,0.73828125 | |
| 82 | +plex,municipality,9,3328,0.030089488148415975,0.00045823265598124517,0.052224433356263526,-0.0009431994798718087,0.01901532501819489,0.03092802726606761,0.7391826923076923 | |
| 83 | +plex,municipality,10,3328,0.030194143109486052,-0.00033243148408436407,0.05221499953743071,-0.0014404225568897607,0.019153754370377817,0.03092370755108541,0.7430889423076923 | |
| 84 | +plex,municipality,11,3328,0.030196699685274782,-0.0005071502270420185,0.0519472724933694,-0.0009267387182001896,0.019627275902071434,0.03058956960222138,0.7409855769230769 | |
| 85 | +plex,municipality,12,3328,0.030019016362902714,0.00029732694559798424,0.05150005703267959,-9.47654462859057e-6,0.019092770944119066,0.029870112776456893,0.7418870192307693 | |
| 86 | +plex,province,1,52,0.0025609731874916717,-0.00013590920564457217,0.0034534763426082977,-0.0015875438365012494,0.0025609731874916665,0.0034534763426082946,0.0 | |
| 87 | +plex,province,2,52,0.0024926043659790893,-0.00009454019214262746,0.0032054734636291324,-0.0014694410088973257,0.0024926043659790685,0.003205473463629128,0.019230769230769232 | |
| 88 | +plex,province,3,52,0.002553714357481464,-0.00004047794281379422,0.003094585608310371,-0.0015220777776656772,0.002553714357481438,0.003094585608310366,0.0 | |
| 89 | +plex,province,4,52,0.0027547193357477986,-0.00015209656244811245,0.003012014534139996,-0.0015026221629895072,0.0027547193357477804,0.0030120145341399926,0.0 | |
| 90 | +plex,province,5,52,0.0027800874380931634,-0.0002161250434366023,0.0029145807667974918,-0.0014055396426533068,0.0027800874380931396,0.002914580766797496,0.0 | |
| 91 | +plex,province,6,52,0.0026934949345003034,-0.0002022008710534018,0.0028829386532136163,-0.0012786384099770268,0.0026934949345002804,0.002882938653213614,0.0 | |
| 92 | +plex,province,7,52,0.002582845125215752,-0.00015788692632138522,0.0028886757938532334,-0.0013242037067489598,0.0025828451252157382,0.0028886757938532395,0.0 | |
| 93 | +plex,province,8,52,0.002639080129391382,-0.00021502241592675158,0.0027037215493154174,-0.0012176184392219242,0.002639080129391367,0.0027037215493154244,0.0 | |
| 94 | +plex,province,9,52,0.0025205385749526854,-0.00011984928174292413,0.002612417507449769,-0.001147538912493115,0.002520538574952671,0.0026124175074497737,0.0 | |
| 95 | +plex,province,10,52,0.002471275260724911,-0.00016212659033303835,0.0025179190218171587,-0.0009736164485255596,0.0024712752607249015,0.002517919021817157,0.0 | |
| 96 | +plex,province,11,52,0.0023786308212119735,0.00008397121580975361,0.002409360630624702,-0.0007613081176061588,0.0023786308212119666,0.002409360630624712,0.0 | |
| 97 | +plex,province,12,52,0.002372654388962432,-0.00017528002044779283,0.0025522750376679062,-0.0008278294224610996,0.0023726543889624327,0.0025522750376679223,0.0 | |
| 98 | +plex,region,1,884,0.013662550268308716,-0.0003106828386291631,0.020509090851500367,-0.004066840589696208,0.008588373597085176,0.013735934909154963,0.5328054298642534 | |
| 99 | +plex,region,2,884,0.01380938733905172,-0.00005507667807821287,0.020321558543735557,-0.004052497859157451,0.008743558386230163,0.013775239910577506,0.5395927601809954 | |
| 100 | +plex,region,3,884,0.013579203115088274,-0.0001558913225179832,0.02036969763369768,-0.004311733216030613,0.008691208480082676,0.013924305443190406,0.5248868778280543 | |
| 101 | +plex,region,4,884,0.013639439516273274,-0.00021329719406405458,0.020611391769852692,-0.0042585236155864474,0.008751234298494903,0.013840468072287818,0.5271493212669683 | |
| 102 | +plex,region,5,884,0.01382307529096038,-0.0003156095454226111,0.021284503765627488,-0.0042184414956753175,0.008739481647510192,0.0142695085422924,0.5328054298642534 | |
| 103 | +plex,region,6,884,0.013543587939921907,-0.0007488494780056366,0.021803628079206022,-0.004280267650932595,0.008605126816542623,0.014021650843498165,0.5316742081447964 | |
| 104 | +plex,region,7,884,0.013396622009093124,-0.0005177203933074814,0.022461321433010415,-0.004277202804897457,0.008728996501200475,0.014374504850619765,0.5226244343891403 | |
| 105 | +plex,region,8,884,0.013425408159991752,-0.0004276021187150886,0.02204763031635865,-0.004091255775097806,0.00864160129784591,0.014318956170876551,0.5237556561085973 | |
| 106 | +plex,region,9,884,0.013700099938324965,-0.0002571476814299577,0.02202374907087782,-0.004011719013280068,0.008795275562324678,0.014197163527242035,0.5328054298642534 | |
| 107 | +plex,region,10,884,0.013954066485638647,-0.0003897843140120213,0.02195315127310212,-0.003781751291640429,0.008956589306037753,0.014001053319166847,0.5452488687782805 | |
| 108 | +plex,region,11,884,0.014203977274158053,-0.000227649828256144,0.02185184168913049,-0.00333122020543886,0.009293019546997147,0.014028442873695876,0.5599547511312217 | |
| 109 | +plex,region,12,884,0.014069327265242619,-0.00044752919725785404,0.021825252254585842,-0.0032864627859346017,0.009161575036737011,0.014281809023605587,0.5497737556561086 | |
added
results/vintages/revisions_long_filtered.parquet
+0 −0
Binary file not shown.
modified
scripts/60_figures.py
+16 −15
@@ -203,26 +203,27 @@ def fig_mc(): | ||
| 203 | 203 | return |
| 204 | 204 | d = pl.read_csv(p) |
| 205 | 205 | methods = ["S_sharp", "S_sharp_filtered", "S_noEB", "S_LLT", "S_coarseloc", "S_norobust", "K_qhpi", "C_pooled_td", "D_rtd13", "B_stratified", "A_median"] |
| 206 | − cols = {m: METHOD_COLORS.get(m.replace("D_rtd13", "D_rtd_w13_mean").replace("B_stratified", "B_stratified_median"), SERIES[i % 8]) for i, m in enumerate(methods)} | |
| 207 | − scen = d["scenario"].unique().sort().to_list() | |
| 208 | − fig, axes = plt.subplots(2, 1, figsize=(9.0, 5.6), sharex=True) | |
| 206 | + labels = {"S_sharp": "SHARP-HPI (smoothed)", "S_sharp_filtered": "SHARP-HPI (filtered)", "S_noEB": "SHARP: no EB pooling", "S_LLT": "SHARP: local linear trend", "S_coarseloc": "SHARP: municipal location FE", "S_norobust": "SHARP: Gaussian", | |
| 207 | + "K_qhpi": "QHPI v2.1 architecture", "C_pooled_td": "Pooled time dummy (cell)", "D_rtd13": "Rolling TD 13 m (cell)", "B_stratified": "Stratified median", "A_median": "Median price"} | |
| 208 | + cols = dict(zip(methods, [SERIES[0], "#0b0b0b", SERIES[1], SERIES[3], SERIES[4], SERIES[6], SERIES[7], SERIES[5], "#7a7a7a", "#a0a0a0", SERIES[2]])) | |
| 209 | + scen = ["baseline", "turning_point", "flat_quality_up", "rising_quality_down", "drifting_hedonics", "heavy_tails", "sparse", "no_local_dynamics", "idiosyncratic_towns"] | |
| 210 | + scen = [s for s in scen if s in d["scenario"].unique().to_list()] | |
| 211 | + fig, axes = plt.subplots(2, 1, figsize=(9.2, 6.0), sharex=True) | |
| 209 | 212 | for ax, lvl in zip(axes, ["municipality", "region"]): |
| 210 | − sub = d.filter(pl.col("level") == lvl) | |
| 211 | − w = 0.8 / len(methods) | |
| 213 | + sub = d.filter(pl.col("level") == lvl); w = 0.8 / len(methods) | |
| 212 | 214 | for j, m in enumerate(methods): |
| 213 | − s = sub.filter(pl.col("method") == m) | |
| 214 | − vals = {r["scenario"]: (r["rmse_ret"], r["rmse_ret_mcse"]) for r in s.iter_rows(named=True)} | |
| 215 | − ys = [vals.get(sc, (np.nan, 0))[0] for sc in scen]; es = [vals.get(sc, (0, 0))[1] for sc in scen] | |
| 216 | − ax.bar(np.arange(len(scen)) + j * w - 0.4 + w / 2, 100 * np.array(ys), width=w * 0.92, color=cols[m], label=METHOD_LABELS.get(m, m), yerr=100 * np.array(es), error_kw=dict(lw=0.5, capsize=0), lw=0) | |
| 217 | − ax.set_ylabel(f"RMSE of monthly log change (%), {lvl}"); ax.set_ylim(0, None) | |
| 218 | − axes[1].set_xticks(range(len(scen))); axes[1].set_xticklabels(scen, rotation=20, fontsize=7.5) | |
| 215 | + s = sub.filter(pl.col("method") == m); vals = {r["scenario"]: (r["rmse_ret"], r["rmse_ret_mcse"]) for r in s.iter_rows(named=True)} | |
| 216 | + ys = [100 * vals[sc][0] if sc in vals and vals[sc][0] is not None else np.nan for sc in scen]; es = [100 * vals[sc][1] if sc in vals and vals[sc][1] is not None else 0 for sc in scen] | |
| 217 | + ax.bar(np.arange(len(scen)) + j * w - 0.4 + w / 2, ys, width=w * 0.92, color=cols[m], label=labels[m], yerr=es, error_kw=dict(lw=0.5, capsize=0), lw=0) | |
| 218 | + ax.set_yscale("log"); ax.set_ylabel(f"RMSE of monthly log change (%, log scale)\n{lvl} level"); ax.grid(True, which="both", axis="y", lw=0.4) | |
| 219 | + axes[1].set_xticks(range(len(scen))); axes[1].set_xticklabels([s.replace("_", " ") for s in scen], rotation=20, fontsize=7.5) | |
| 219 | 220 | axes[0].legend(ncol=4, fontsize=6.5, loc="upper left") |
| 220 | 221 | save(fig, "fig_mc_rmse") |
| 221 | − fig, ax = plt.subplots(figsize=(9.0, 2.8)) | |
| 222 | − for j, m in enumerate(["S_sharp", "S_sharp_filtered", "S_noEB", "S_LLT", "K_qhpi", "C_pooled_td"]): | |
| 222 | + fig, ax = plt.subplots(figsize=(9.2, 2.9)) | |
| 223 | + for m in ["S_sharp", "S_sharp_filtered", "S_noEB", "S_LLT", "S_norobust", "S_coarseloc", "K_qhpi", "C_pooled_td"]: | |
| 223 | 224 | s = d.filter((pl.col("level") == "municipality") & (pl.col("method") == m)); vals = {r["scenario"]: r["cov95"] for r in s.iter_rows(named=True)} |
| 224 | − ax.plot(range(len(scen)), [100 * vals.get(sc, np.nan) for sc in scen], marker="o", ms=4, color=cols[m], label=METHOD_LABELS.get(m, m)) | |
| 225 | − ax.axhline(95, color=INK2, lw=0.6, ls="--"); ax.set_ylabel("Coverage of 95 % intervals (%), municipalities"); ax.set_xticks(range(len(scen))); ax.set_xticklabels(scen, rotation=20, fontsize=7.5); ax.legend(ncol=3, fontsize=6.5) | |
| 225 | + ax.plot(range(len(scen)), [100 * vals.get(sc, np.nan) if vals.get(sc) is not None else np.nan for sc in scen], marker="o", ms=4, color=cols[m], label=labels[m]) | |
| 226 | + ax.axhline(95, color=INK2, lw=0.6, ls="--"); ax.set_ylabel("Coverage of 95 % intervals (%)\nmunicipality level"); ax.set_xticks(range(len(scen))); ax.set_xticklabels([s.replace("_", " ") for s in scen], rotation=20, fontsize=7.5); ax.legend(ncol=4, fontsize=6.5) | |
| 226 | 227 | save(fig, "fig_mc_coverage") |
| 227 | 228 | |
| 228 | 229 | |
modified
scripts/61_tables.py
+10 −5
@@ -260,6 +260,10 @@ def t_benchmarks(): | ||
| 260 | 260 | notes="Repeat sales are an independent (pair-based) arbiter, not the truth: they carry renovation and selection biases. Lower s.d. is not better per se; the Monte Carlo (Section 7) separates noise from signal.") |
| 261 | 261 | |
| 262 | 262 | |
| 263 | +def fv(x, fmt, scale=1.0): | |
| 264 | + return "—" if x is None or (isinstance(x, float) and x != x) else fmt.format(scale * x) | |
| 265 | + | |
| 266 | + | |
| 263 | 267 | def t_simulation(): |
| 264 | 268 | if not ok(R / "simulation" / "summary.csv"): |
| 265 | 269 | return |
@@ -275,9 +279,10 @@ def t_simulation(): | ||
| 275 | 279 | r = [lab.get(m, m)] |
| 276 | 280 | for sc in scen: |
| 277 | 281 | x = d.filter((pl.col("scenario") == sc) & (pl.col("level") == lvl) & (pl.col("method") == m)) |
| 278 | − r.append(f"{100*x['rmse_ret'][0]:.2f} ({100*x['rmse_ret_mcse'][0]:.2f})" if x.height and x["rmse_ret"][0] == x["rmse_ret"][0] else "—") | |
| 282 | + r.append(f"{fv(x['rmse_ret'][0], '{:.2f}', 100)} ({fv(x['rmse_ret_mcse'][0], '{:.2f}', 100)})" if x.height else "—") | |
| 279 | 283 | if x.height: |
| 280 | − macro(f"MC{lvl}{m}{sc}Rmse", 100 * x["rmse_ret"][0], "{:.2f}"); macro(f"MC{lvl}{m}{sc}Cov", 100 * x["cov95"][0], "{:.0f}"); macro(f"MC{lvl}{m}{sc}Bias", 100 * x["bias_ret"][0], "{:.3f}") | |
| 284 | + macro(f"MC{lvl}{m}{sc}Rmse", 100 * x["rmse_ret"][0] if x["rmse_ret"][0] is not None else None, "{:.2f}"); macro(f"MC{lvl}{m}{sc}Cov", 100 * x["cov95"][0] if x["cov95"][0] is not None else None, "{:.0f}"); macro(f"MC{lvl}{m}{sc}Bias", 100 * x["bias_ret"][0] if x["bias_ret"][0] is not None else None, "{:.3f}") | |
| 285 | + macro(f"MC{lvl}{m}{sc}Share", 100 * x["share_est"][0] if x["share_est"][0] is not None else None, "{:.0f}"); macro(f"MC{lvl}{m}{sc}Lag", x["lag_xcorr"][0], "{:+.2f}"); macro(f"MC{lvl}{m}{sc}Rev", 100 * x["revision_rmse"][0] if x["revision_rmse"][0] is not None else None, "{:.2f}") | |
| 281 | 286 | rows.append(r) |
| 282 | 287 | table(f"tab_mc_rmse_{lvl}", ["Method"] + [s.replace("_", " ") for s in scen], rows, f"Monte Carlo: RMSE of monthly log changes (×100) at the {lvl} level, by scenario (Monte Carlo standard error in parentheses)", f"tab:mc_rmse_{lvl}", size="\\scriptsize", longtable=False, |
| 283 | 288 | notes="Averages over cells and replications; see Table~\\ref{tab:mc_design} for the scenarios. Lower is better; the final two scenarios are the deliberate counter-examples.") |
@@ -286,7 +291,7 @@ def t_simulation(): | ||
| 286 | 291 | r = [lab.get(m, m)] |
| 287 | 292 | for sc in scen: |
| 288 | 293 | x = d.filter((pl.col("scenario") == sc) & (pl.col("level") == "municipality") & (pl.col("method") == m)) |
| 289 | − r.append(f"{100*x['cov95'][0]:.0f}" if x.height and x["cov95"][0] == x["cov95"][0] else "—") | |
| 294 | + r.append(fv(x["cov95"][0], "{:.0f}", 100) if x.height else "—") | |
| 290 | 295 | rows.append(r) |
| 291 | 296 | table("tab_mc_coverage", ["Method"] + [s.replace("_", " ") for s in scen], rows, "Monte Carlo: empirical coverage (\\%) of nominal 95\\% intervals for municipal index levels", "tab:mc_coverage", size="\\scriptsize") |
| 292 | 297 | rows = [] |
@@ -294,14 +299,14 @@ def t_simulation(): | ||
| 294 | 299 | r = [lab.get(m, m)] |
| 295 | 300 | for sc in scen: |
| 296 | 301 | x = d.filter((pl.col("scenario") == sc) & (pl.col("level") == "municipality") & (pl.col("method") == m)) |
| 297 | − r.append(f"{100*x['bias_ret'][0]:+.3f}" if x.height else "—") | |
| 302 | + r.append(fv(x["bias_ret"][0], "{:+.3f}", 100) if x.height else "—") | |
| 298 | 303 | rows.append(r) |
| 299 | 304 | table("tab_mc_bias", ["Method"] + [s.replace("_", " ") for s in scen], rows, "Monte Carlo: mean bias of monthly log changes (×100) at the municipality level", "tab:mc_bias", size="\\scriptsize") |
| 300 | 305 | rows = [] |
| 301 | 306 | for sc in scen: |
| 302 | 307 | x = d.filter((pl.col("scenario") == sc) & (pl.col("level") == "municipality") & (pl.col("method") == "S_sharp")) |
| 303 | 308 | y = d.filter((pl.col("scenario") == sc) & (pl.col("level") == "municipality") & (pl.col("method") == "K_qhpi")) |
| 304 | − rows.append([sc.replace("_", " "), str(int(x["reps"][0])), f"{x['lag_xcorr'][0]:+.2f}", f"{100*x['revision_rmse'][0]:.2f}", f"{y['lag_xcorr'][0]:+.2f}" if y.height else "—", f"{x['secs'][0]:.1f}" if x["secs"][0] == x["secs"][0] else "—"]) | |
| 309 | + rows.append([sc.replace("_", " "), str(int(x["reps"][0])), fv(x["lag_xcorr"][0], "{:+.2f}"), fv(x["revision_rmse"][0], "{:.2f}", 100), fv(y["lag_xcorr"][0], "{:+.2f}") if y.height else "—", fv(x["secs"][0], "{:.1f}")]) | |
| 305 | 310 | table("tab_mc_lag", ["Scenario", "Reps", "SHARP lag (months)", "SHARP revision RMSE", "QHPI lag", "sec./rep"], rows, "Monte Carlo: timing diagnostics at the municipality level", "tab:mc_lag", size="\\small", |
| 306 | 311 | notes="Lag = argmax of the cross-correlation between estimated and true 3-month changes (negative = estimate lags). Revision RMSE = RMSE between filtered (real-time) and smoothed monthly changes (×100).") |
| 307 | 312 | |
| 308 | 313 | |