test: anchor LPDiD regression-adjustment SE against Stata teffects (first Stata parity arm)

.gitignore

@@ -120,3 +120,8 @@ tools/reviewer-eval/runs/
 benchmarks/refresh_2026_07/venvs/
 benchmarks/refresh_2026_07/results/raw/
 benchmarks/refresh_2026_07/results/refresh_results_smoke.json
+
+# Stata batch mode writes <basename>.log to the cwd (repo root) - never committed;
+# the golden JSON it produces IS committed (benchmarks/data/lpdid_ra_stata_golden.json).
+# Root-anchored to the generator's own log so it doesn't hide logs elsewhere.
+/generate_lpdid_ra_golden.log

CHANGELOG.md

@@ -7,6 +7,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+- **LP-DiD regression-adjustment SE now anchored against Stata `teffects` (no library
+  behavior change).** The RA influence-function cluster SE has no runnable R reference
+  (`alexCardazzi/lpdid` does direct covariate inclusion, not RA), so it was previously
+  pinned against itself + MC-coverage-validated. It is now cross-validated against Stata
+  `teffects ra ... atet vce(cluster)` via a committed golden
+  (`benchmarks/stata/generate_lpdid_ra_golden.do` → `lpdid_ra_stata_golden.json`;
+  `tests/test_lpdid_ra_stata_parity.py`): the library matches `teffects` to ~1e-16 at all
+  7 event-study horizons, confirming the no-finite-sample-factor convention (REGISTRY
+  LPDiD Deviation 2). This is the repo's first Stata parity arm — `teffects` is native
+  (no SSC dependency) and goldens are committed, so CI needs no Stata.
+
 ### Changed
 - **Internal tracking docs reorganized (no library behavior change).** `TODO.md` is now
   the actionable backlog only; blocked / parked work and won't-fix decisions moved to the

DEFERRED.md

@@ -57,7 +57,7 @@ exists but parity can't be verified without a local toolchain.
 | `CallawaySantAnna` bootstrap: align p-value computation with R `did`'s symmetric-percentile method (former "CallawaySantAnna Bootstrap Improvements" section). | `staggered.py` | — | Low |
 | **`bias_corrected_local_linear` (lprobust) Phase-1c follow-ups:** extend golden parity to `kernel ∈ {triangular, uniform}` (epa-only today); expose `vce ∈ {hc0,hc1,hc2,hc3}` on the public wrapper once R goldens exist (port supports all four; needs a per-mode generator + a hc2/hc3 q-fit-leverage decision); clustered-DGP auto-bandwidth parity is **blocked upstream** on an nprobust singleton-cluster bug in `lpbwselect.mse.dpi` (Phase-1c DGP 4 uses manual `h=b=0.3`). | `_nprobust_port.py`, `local_linear.py`, `generate_nprobust_lprobust_golden.R` | Phase 1c | Low-Med |
 | `HeterogeneousAdoptionDiD` Stute-family Stata-bridge parity: no public R `Stutetest` package exists; would add `benchmarks/stata/generate_stute_golden.do` + a Stata dependency. | `benchmarks/stata/`, `tests/test_stute_test_parity.py` | follow-up | Low |
-| **`LPDiD` regression-adjustment SE — no runnable R reference.** The RA influence-function cluster SE is canonically Stata `teffects ra ... atet vce(cluster)` only; no R package computes it (`alexCardazzi/lpdid` does direct covariate inclusion, not RA). Today the RA *point* is R-anchored (~1e-12), the SE is pinned + MC-coverage-validated (`coverage_lpdid_ra.py`). Follow-up: contribute the RA path to `alexCardazzi/lpdid` so a runnable R RA reference exists — only a *trusted* anchor once cross-checked vs Stata `teffects` (else circular). | `tests/test_methodology_lpdid.py`, `benchmarks/python/coverage_lpdid_ra.py` | #B2 follow-up | Low |
+| **`LPDiD` regression-adjustment SE — Stata-`teffects` anchored; optional R follow-up only.** The RA influence-function cluster SE is canonically Stata `teffects ra ... atet vce(cluster)` (no R package computes it — `alexCardazzi/lpdid` does direct covariate inclusion, not RA). **Resolved:** `benchmarks/stata/generate_lpdid_ra_golden.do` reconstructs each horizon's clean sample independently and anchors the SE against `teffects` to ~1e-16 at all 7 horizons (golden `lpdid_ra_stata_golden.json`; `tests/test_lpdid_ra_stata_parity.py`, which also gates the Stata row count vs the library `n_obs`); the point is triple-anchored (Python/R/Stata). Stata is node-locked (not in CI), so the CI-runnable guards remain the regression pin (`RA_SE_PIN`) + MC coverage (`coverage_lpdid_ra.py`). *Optional residual (no longer a blocker):* contribute an RA path to `alexCardazzi/lpdid` so a runnable **R** reference also exists. | `benchmarks/stata/generate_lpdid_ra_golden.do`, `tests/test_lpdid_ra_stata_parity.py` | #B2 · Stata arm | Low |
 | **`LPDiD` survey scope gaps (PR-D1 deferrals).** Survey support covers the variance-weighted default path only. (a) `survey_design` + `reweight=True` (the equally-weighted / regression-adjustment IF path) is rejected: the weighted RA influence-function variance has **no runnable survey reference** (same class as the RA-SE row above - `survey::svyglm` anchors only the OLS/WLS path). (b) Replicate-weight survey designs (BRR/Fay/JK1/JKn/SDR) and (c) non-pweight (fweight/aweight) types are rejected pending demand. | `lpdid.py`, REGISTRY #8 | PR-D1 | Low |
 | **`LPDiD` non-absorbing SE/RA external-reference gap.** The non-absorbing *point* estimands are R-parity-locked (PR-C2: independent `fixest::feols` Eq. 12/13 reconstruction, point+SE ~1e-13/~1e-15 vw; `effect_stabilization` reweighted point + pinned SE), but the authors' canonical non-absorbing SE/RA is Stata `lpdid`/`teffects` only - no faithful R analogue exists (`alexCardazzi/lpdid`'s `nonabsorbing_lag` proved NOT a faithful Eq. 13: off-switch clamp + non-paper boundary/placebo window, diverges ~0.01-0.05 even on a monotone panel; recorded as a divergent reference, not a gate). Revisit if a Stata toolchain or a corrected R package appears - same class as the absorbing RA-SE row above. | `benchmarks/R/generate_lpdid_golden.R`, `tests/test_methodology_lpdid.py` | PR-C2 | Low |
 | `HeterogeneousAdoptionDiD` Phase-3 R-parity: ships coverage-rate validation on synthetic DGPs, not tight point parity vs `chaisemartin::stute_test` / `yatchew_test` (needs bootstrap-seed-semantics + `B` alignment across numpy/R). | `tests/test_had_pretests.py` | Phase 3 | Low |

benchmarks/README.md

@@ -43,6 +43,21 @@ Required R packages:
 - `jsonlite` - JSON interchange
 - `data.table` - Fast data manipulation
 
+### Stata
+
+A few goldens are anchored against Stata where no runnable R reference exists.
+Stata is node-locked single-user, so — like the R arm — the goldens are committed
+and CI never needs Stata. Generators live in `stata/` and are run headless:
+
+```bash
+# macOS, StataSE 19 (binary is not on PATH by default)
+/Applications/Stata/StataSE.app/Contents/MacOS/stata-se -b do \
+    benchmarks/stata/generate_lpdid_ra_golden.do
+```
+
+Current arm uses only **native** Stata commands (`teffects`) — no SSC packages —
+so `version 19` pins behavior. See `stata/README.md`.
+
 ## Directory Structure
 
 ```
@@ -56,6 +71,9 @@ benchmarks/
 │   ├── benchmark_synthdid.R  # Synthetic DiD
 │   ├── benchmark_honest.R    # HonestDiD
 │   └── benchmark_fixest.R    # Basic DiD / TWFE
+├── stata/
+│   ├── README.md                       # Stata arm docs
+│   └── generate_lpdid_ra_golden.do     # LPDiD RA SE vs teffects ra
 ├── python/
 │   ├── utils.py              # Common utilities
 │   ├── benchmark_callaway.py # CallawaySantAnna
@@ -72,11 +90,12 @@ benchmarks/
 
 ## Estimator Comparisons
 
-| diff-diff | R Package | Reference | Status |
+| diff-diff | Reference Package | Reference | Status |
 |-----------|-----------|-----------|--------|
 | `CallawaySantAnna` | `did::att_gt` | Callaway & Sant'Anna (2021) | ✓ Integrated |
 | `SyntheticDiD` | `synthdid::synthdid_estimate` | Arkhangelsky et al. (2021) | ✓ Integrated |
 | `DifferenceInDifferences` | `fixest::feols` | Standard DiD | ✓ Integrated |
+| `LPDiD` (RA SE) | Stata `teffects ra ... atet` | Dube, Girardi, Jorda & Taylor (2025) | ✓ Integrated |
 | `HonestDiD` | `HonestDiD::createSensitivityResults` | Rambachan & Roth (2023) | Planned |
 
 Note: HonestDiD benchmark scripts exist but are not yet integrated into the main runner.

benchmarks/data/lpdid_ra_stata_golden.json

@@ -0,0 +1,24 @@
+{
+  "meta": {
+    "estimator": "LPDiD regression-adjustment (RA) SE - Stata teffects ra atet",
+    "generator": "benchmarks/stata/generate_lpdid_ra_golden.do",
+    "source_panel": "benchmarks/data/lpdid_test_panel.csv",
+    "point_anchor": "benchmarks/data/lpdid_golden.json ra_cov[h][0]",
+    "stata_edition": "SE",
+    "cmd": "teffects ra (Dy x i.time) (tdiff), atet vce(cluster unit)",
+    "se_convention": "cluster-robust IF at unit, NO finite-sample factor (teffects convention); t(G-1) reference on the library side",
+    "note": "Independent Stata reconstruction of the per-horizon clean sample (ports generate_lpdid_golden.R prep/clean_h). N and G are emitted to power the Python row-count sample-integrity gate.",
+    "stata_version": 19.0,
+    "pre_window": 3,
+    "post_window": 4
+  },
+  "ra_se": {
+    "0": {"att": 2.888871838355342, "se": 0.45516589747218866, "N": 522, "G": 60},
+    "1": {"att": 3.225268910679945, "se": 0.44522904812910252, "N": 461, "G": 60},
+    "2": {"att": 3.5779502216280248, "se": 0.46179396866595729, "N": 401, "G": 60},
+    "3": {"att": 3.973278128236954, "se": 0.39295354484873846, "N": 353, "G": 60},
+    "4": {"att": 4.2651037814454096, "se": 0.39398602601018534, "N": 305, "G": 60},
+    "-2": {"att": 0.1204444745695894, "se": 0.43592937799131937, "N": 461, "G": 60},
+    "-3": {"att": -0.11970460945194859, "se": 0.43296260860283592, "N": 401, "G": 60}
+  }
+}

benchmarks/stata/README.md

@@ -0,0 +1,133 @@
+# Stata parity benchmarks
+
+Stata golden generators live here. They are the **first Stata arm** in the repo;
+the pattern mirrors `benchmarks/R/` (a `generate_*` script writes a committed
+golden JSON that a skip-guarded `tests/test_*_parity.py` reads, so CI never needs
+Stata). Stata is node-locked single-user, so — exactly like the R arm — goldens
+are committed and only regenerated locally.
+
+Locating the binary (macOS, StataSE 19; **not** on `PATH` by default):
+
+```
+/Applications/Stata/StataSE.app/Contents/MacOS/stata-se
+```
+
+---
+
+# `teffects ra` parity for the LP-DiD regression-adjustment SE
+
+`benchmarks/stata/generate_lpdid_ra_golden.do` produces
+`benchmarks/data/lpdid_ra_stata_golden.json`, consumed by
+`tests/test_lpdid_ra_stata_parity.py` to verify that diff-diff's LP-DiD
+regression-adjustment (RA) standard error (Dube, Girardi, Jorda & Taylor 2025)
+matches Stata `teffects ra ... atet vce(cluster)` across the 7 event-study
+horizons.
+
+## Why Stata `teffects`
+
+The RA covariate path reports an influence-function cluster variance with **no
+finite-sample factor**. **No R package computes it** — `alexCardazzi/lpdid` uses
+direct covariate inclusion, not RA — so there is no runnable R analogue. The
+canonical reference is Stata `teffects ra ... atet vce(cluster)`, which the LP-DiD
+authors themselves invoke (paper footnote 9). `teffects` is a **native** Stata
+command (no SSC package), so `version 19` in the generator fully pins its
+numerical behavior; there is no third-party package version to track.
+
+This arm converts REGISTRY `## LPDiD` Deviation 2 from an *inference* (the
+no-finite-sample-factor convention was argued from degrees-of-freedom comments in
+the authors' `.do` files) into a *measurement*: the library RA IF SE matches
+`teffects` to ~1e-16 at every horizon (same machine / BLAS).
+
+## What the generator does
+
+It reads (does **not** regenerate) `benchmarks/data/lpdid_test_panel.csv` — whose
+sole owner is `benchmarks/R/generate_lpdid_golden.R` — and **independently
+reconstructs** each horizon's clean sample by porting the R `prep` + `clean_h`
+recipe (`generate_lpdid_golden.R:97-123`) to `tsset` + `L.`/`F.` time-series
+operators. This gives three independent sample constructions (Python / R / Stata);
+a port bug is very likely to be caught before it could masquerade as an SE finding,
+because the Python test's point gate (Stata ATET vs the R-anchored `ra_cov[h][0]`) and
+sample-shape gate (`(e(N), e(N_clust))` vs the library's `(n_obs, n_clusters)`) would
+fail first.
+
+`tsfill` is load-bearing: it mirrors R `fill_gaps()` so the absorbing `treat`
+recompute and the long differences see the completed calendar grid across the
+deliberate interior gap at `(unit==60, time==7)`.
+
+The per-horizon command is:
+
+```stata
+teffects ra (Dy x i.time) (tdiff), atet vce(cluster unit)
+```
+
+(The authors' footnote-9 syntax omits the covariate; the RA *covariate* path adds
+`x` to the outcome model, as here.)
+
+## Regenerating
+
+```bash
+# from the repo root
+/Applications/Stata/StataSE.app/Contents/MacOS/stata-se -b do \
+    benchmarks/stata/generate_lpdid_ra_golden.do
+
+# batch mode ALWAYS exits 0 — verify the log has no Stata errors:
+grep -E '^r\([0-9]+\);' generate_lpdid_ra_golden.log   # must print nothing
+```
+
+The output JSON is **committed** so CI doesn't need Stata. Only re-run when the
+committed panel changes (then also refresh `RA_SE_PIN` in
+`tests/test_methodology_lpdid.py`) or the horizon set changes.
+
+## Skip behavior
+
+`tests/test_lpdid_ra_stata_parity.py` calls `pytest.skip(...)` when the golden (or
+the shared panel / R golden it cross-checks against) is absent, so CI passes
+without Stata. The estimator itself is exercised independently by
+`tests/test_methodology_lpdid.py` and `tests/test_lpdid.py`.
+
+## JSON schema
+
+```json
+{
+  "meta": {
+    "estimator": "LPDiD regression-adjustment (RA) SE - Stata teffects ra atet",
+    "generator": "benchmarks/stata/generate_lpdid_ra_golden.do",
+    "source_panel": "benchmarks/data/lpdid_test_panel.csv",
+    "point_anchor": "benchmarks/data/lpdid_golden.json ra_cov[h][0]",
+    "stata_edition": "SE",
+    "cmd": "teffects ra (Dy x i.time) (tdiff), atet vce(cluster unit)",
+    "se_convention": "...",
+    "note": "...",
+    "stata_version": 19.0,
+    "pre_window": 3,
+    "post_window": 4
+  },
+  "ra_se": {
+    "0":  {"att": <float>, "se": <float>, "N": <int>, "G": <int>},
+    "...": "post {0..4} then pre {-2,-3}; h=-1 is the omitted reference"
+  }
+}
+```
+
+Values are written at `%21.17g` (round-trip-exact IEEE-754 double). `N` and `G`
+(`e(N)` / `e(N_clust)`) are emitted so the Python gate can assert the Stata clean
+sample matches the library's realized size and cluster count — which, together with
+the point (1e-10) and SE (~1e-16) agreement, strongly corroborates the same clean
+sample. No timestamp — the golden regenerates byte-identically.
+
+## Known constraints
+
+- **Batch mode always exits 0**, even on a hard error (`r(NNN);`). Never trust the
+  shell exit code — parse the `.log` for `^r\([0-9]+\);` (the generator also runs
+  an in-`.do` point smoke gate at 1e-8 that surfaces as `r(9);` on a gross port
+  bug; the Python test's 1e-10 gate is authoritative).
+- **`c(flavor)` misreports the edition** as `IC` on StataSE, and `c(edition)` is
+  unreliable. The generator derives the edition from the `c(MP)` / `c(SE)` 0/1 flags,
+  with BE by elimination (`c(BE)` is undefined and `cond()` evaluates all branches
+  eagerly, so it must not be referenced). `"SE"` is simply the committed golden's
+  current value.
+- **SSC has no version history.** `ssc install` always fetches latest and there is
+  no lockfile / archive to pin against. This arm is exempt because `teffects` is
+  native (pinned by `version 19`), but any *future* SSC-dependent Stata generator
+  must record `which <pkg>` output verbatim into its golden's `meta` so drift is
+  at least detectable.