# F6-conf-02 — BoC March-2020 emergency cut and the 5-year GoC yield

## Question
Estimand: the average one-day change of the Government of Canada 5-year benchmark yield (bp) on trading days flagged `post_cut = 1` (on/after 2020-03-04), minus the average one-day change on the 120 trading days just before the first flagged day. It comes with a 95% Welch (unequal-variance) interval. **The estimand cannot be computed from the file provided. The conclusion is `premise_invalid` (`conclusion`), with `feasibility` = `infeasible`.**

## Data and exclusions
- `data/gc5y_daily.csv`: 2498 rows (`n_input_rows`). SHA-256 matches the README. There are no missing values, no duplicate dates, the dates are strictly increasing, and the gaps match business-daily data (full audit in `out/state/data_audit.md`).
- The sample runs from 2010-01-04 (`sample_start`) to 2019-12-31 (`sample_end`). The README confirms the series was retrieved with `end_date=2019-12-31`.
- **No row has `post_cut = 1` (`n_treated` = 0).** The file ends more than two months before the 2020-03-04 cut. So the post-cut window is empty, and the 120-day pre-window cannot be defined because it is anchored on the first flagged day. This is recorded in `data_issues`.
- Exclusion ledger (`out/exclusions.csv`): 2498 rows loaded. Taking first differences leaves 2497 one-day changes (the baseline sample). Selecting the estimation windows leaves 0 rows. `n_analysed` is therefore null.

## Method
- One-day change = 100·(y_t − y_{t−1}) between consecutive rows, dated t. `post_cut` is used exactly as provided.
- Baseline: the mean one-day change over the whole file.
- Planned main method, implemented in `out/analysis.py` but not executable on this file: difference in means between the post window and the pre window, with a Welch-Satterthwaite t interval. `model_metric` would be the RMSE of the two-window mean model.
- I deliberately used no proxy. Examples would be treating the last 120 days of 2019 as "pre" or picking another event date. Either would redefine the treatment, which `TASK.md` forbids. No randomness is used (`random_seed` = null).

## Results
- Baseline mean one-day change over 2010–2019: −0.0429 bp/day (`baseline_metric`). This equals the total 2010→2019 decline (2.75% → 1.68%) divided by 2497 changes.
- `effect_estimate`, `effect_ci_low`, `effect_ci_high` and `model_metric` are null: not estimable.
- `conclusion` = `premise_invalid`. The reason is given in `feasibility_reason`.
- Figure 1 (`out/figure1.png`) plots the yield over 2010–2019. Table 1 (`out/table1.csv`) gives yearly n_days, mean yield and mean change. These outputs describe the sample only and do not inform the estimand.

## Uncertainty
No interval can be reported for the effect (`effect_ci_low`/`effect_ci_high` are null). The baseline (`baseline_metric`) is a descriptive average, and I attach no inferential interval to it. Daily yield changes are autocorrelated and heteroskedastic, so a naive i.i.d. standard error would understate its uncertainty.

## Limitations and open issues
- **Main failure:** the input lacks the treatment period entirely, so the scientific question cannot be answered from it. The fix would be to re-retrieve BD.CDN.5YR.DQ.YLD with an end date after March 2020. I did not do this because there is no network access and only the provided file may be used.
- If corrected data were supplied, these threats would remain:
  - The windows overlap the COVID-19 market shock. A pre/post difference in means would mix the policy cut with flight-to-quality, liquidity stress and further cuts (2020-03-13, 2020-03-27) and asset purchases, so it is not a causal effect of the 2020-03-04 cut.
  - The Welch interval assumes independent daily changes within each window. Volatility clustering in March 2020 would make it too narrow.
  - The result could change with the length of the post window (all flagged days through the end of the file).
- Not checked: the yields against an independent source; whether yields published after release were revised; and whether `post_cut` flags were set correctly for 2020 dates (none exist here).