1% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json2\begin{table}[htbp]\centering\caption{Robustness diagnostics of the structural-change analysis of the total monthly series.}\label{tab:p9-total-robustness}3\begin{threeparttable}\small4\begin{tabular}{lr}5\toprule6\textbf{Diagnostic} & \textbf{Total series} \\7\midrule8Observations (months), first to last complete month & 101 (2018-02 to 2026-06) \\9Placebo rank of the level $t$-statistic (all candidate months) & rank 63 of 65, placebo $p$ = 0.969 \\10Placebo rank of the slope $t$-statistic (all candidate months) & rank 1 of 65, placebo $p$ = 0.015 \\11Placebo $p$, baseline candidate months only (level; slope) & 1.000; 0.000 (n = 34; zero is attainable) \\12Donut ITS, $\pm$3 months removed: level change (HAC $p$) & +0.049 (0.057), n = 94 \\13Donut ITS: slope change (HAC $p$) & +0.0110 (7e-24) \\14Bai--Perron break dates, BIC (LWZ) & 2022-04-01 (2022-04-01); m = 1, h = 15 \\15Bai--Perron BIC by number of breaks m = 0, 1, 2, 3 & -463.9, -555.3, -551.6, -546.0 \\16PELT changepoints (piecewise-constant mean) & 2023-05, 2024-12 \\17BOCPD: max P(run length $\le$ 3) over 2022-12 to 2023-03; MAP resets & 0.033; none \\18Last complete month 2026-03: level (p); slope (p); Bai--Perron & -0.009 (0.723); +0.0108 (1.1e-30); 2022-04-01 \\19Last complete month 2026-08: level (p); slope (p); Bai--Perron & +0.004 (0.870); +0.0098 (7.4e-18); 2022-02-01 \\20\bottomrule21\end{tabular}22\begin{tablenotes}\footnotesize\item Placebo ranks follow equation~\eqref{eq:placebo}; the baseline-only rank excludes the true event from the candidate set, so a value of zero means that no baseline month yields a larger $t$-statistic. Bai--Perron on the seasonally adjusted log series with trimming $h$; PELT with the $\ell_2$ cost; BOCPD with hazard 1/60 (Part IV).\end{tablenotes}23\end{threeparttable}\end{table}24