HAD Phase 2b: multi-period event-study extension (Appendix B.2)

TODO.md

@@ -89,15 +89,16 @@ Deferred items from PR reviews that were not addressed before merge.
 | `bias_corrected_local_linear`: support `weights=` once survey-design adaptation lands. nprobust's `lprobust` has no weight argument so there is no parity anchor; derivation needed. | `diff_diff/local_linear.py`, `diff_diff/_nprobust_port.py::lprobust` | Phase 1c | Medium |
 | `bias_corrected_local_linear`: support multi-eval grid (`neval > 1`) with cross-covariance (`covgrid=TRUE` branch of `lprobust.R:253-378`). Not needed for HAD but useful for multi-dose diagnostics. | `diff_diff/_nprobust_port.py::lprobust` | Phase 1c | Low |
 | Clustered-DGP parity: Phase 1c's DGP 4 uses manual `h=b=0.3` to sidestep an nprobust-internal singleton-cluster bug in `lpbwselect.mse.dpi`'s pilot fits. Once nprobust ships a fix (or we derive one independently), add a clustered-auto-bandwidth parity test. | `benchmarks/R/generate_nprobust_lprobust_golden.R` | Phase 1c | Low |
-| `HeterogeneousAdoptionDiD` Phase 2b: multi-period event-study extension (Appendix B.2). `aggregate="event_study"` raises `NotImplementedError` in Phase 2a; Phase 2b will aggregate per-cohort first-differences into an event-study result with `.event_study_effects` / `.event_study_se` result fields. | `diff_diff/had.py`, `tests/test_had.py` | Phase 2a | Medium |
+| `HeterogeneousAdoptionDiD` joint cross-horizon covariance on event study: per-horizon SEs use INDEPENDENT sandwiches in Phase 2b (paper-faithful pointwise CIs per Pierce-Schott Figure 2). A follow-up could derive an IF-based stacking of per-horizon scores for joint cross-horizon inference (needed for joint hypothesis tests across event-time horizons). Block-bootstrap is a reasonable alternative. | `diff_diff/had.py::_fit_event_study` | Phase 2b | Low |
+| `HeterogeneousAdoptionDiD` event-study staggered-timing beyond last cohort: Phase 2b auto-filters staggered panels to the last cohort per paper Appendix B.2. Earlier-cohort treatment effects are not identified by HAD; redirecting to `ChaisemartinDHaultfoeuille` / `did_multiplegt_dyn` is the paper's prescription. A full staggered HAD would require a different identification path (out of paper scope). | `diff_diff/had.py::_validate_had_panel_event_study` | Phase 2b | Low |
 | `HeterogeneousAdoptionDiD`: survey-design integration (`survey=SurveyDesign(...)`). Currently raises `NotImplementedError`. Requires Taylor-linearization of the β-scale rescaling and replicate-weight-compatible 2SLS variance on the mass-point path. | `diff_diff/had.py` | Phase 2a | Medium |
 | `HeterogeneousAdoptionDiD`: `weights=` support. Deferred jointly with survey integration. nprobust's `lprobust` has no weight argument so the nonparametric continuous path needs a derivation; the 2SLS mass-point path needs weighted-sandwich parity. | `diff_diff/had.py` | Phase 2a | Medium |
 | `HeterogeneousAdoptionDiD` mass-point: `vcov_type in {"hc2", "hc2_bm"}` raises `NotImplementedError` pending a 2SLS-specific leverage derivation. The OLS leverage `x_i' (X'X)^{-1} x_i` is wrong for 2SLS; the correct finite-sample correction uses `x_i' (Z'X)^{-1} (...) (X'Z)^{-1} x_i`. Needs derivation plus an R / Stata (`ivreg2 small robust`) parity anchor. | `diff_diff/had.py::_fit_mass_point_2sls` | Phase 2a | Medium |
 | `HeterogeneousAdoptionDiD` continuous paths: thread `cluster=` through `bias_corrected_local_linear` (Phase 1c's wrapper already supports cluster; Phase 2a ignores it with a `UserWarning` on the continuous path to keep scope tight). | `diff_diff/had.py`, `diff_diff/local_linear.py` | Phase 2a | Low |
 | `HeterogeneousAdoptionDiD` Phase 3: `qug_test()`, `stute_test()`, `yatchew_hr_test()` pre-test diagnostics (paper Section 3.3). Composite helper `did_had_pretest_workflow()`. Not part of Phase 2a scope. | `diff_diff/had.py`, new module | Phase 2a | Medium |
 | `HeterogeneousAdoptionDiD` Phase 4: Pierce-Schott (2016) replication harness; reproduce paper Figure 2 values and Table 1 coverage rates. | `benchmarks/`, `tests/` | Phase 2a | Low |
 | `HeterogeneousAdoptionDiD` Phase 5: `practitioner_next_steps()` integration, tutorial notebook, and `llms.txt` updates (preserving UTF-8 fingerprint). | `diff_diff/practitioner.py`, `tutorials/`, `diff_diff/guides/` | Phase 2a | Low |
-| `HeterogeneousAdoptionDiD` staggered-timing reduction: Phase 2a requires exactly 2 time periods and raises on `>2` periods with or without `first_treat_col`. A "last-cohort subgroup" reduction scheme (slice to max-cohort's 2-period window) could lift this in a targeted follow-up PR before full Phase 2b multi-period aggregation. | `diff_diff/had.py::_validate_had_panel` | Phase 2a | Low |
+| `HeterogeneousAdoptionDiD` time-varying dose on event study: Phase 2b REJECTS panels where `D_{g,t}` varies within a unit for `t >= F` (the aggregation uses `D_{g, F}` as the single regressor for all horizons, paper Appendix B.2 constant-dose convention). A follow-up PR could add a time-varying-dose estimator for these panels; current behavior is front-door rejection with a redirect to `ChaisemartinDHaultfoeuille`. | `diff_diff/had.py::_validate_had_panel_event_study` | Phase 2b | Low |
 | `HeterogeneousAdoptionDiD` repeated-cross-section support: paper Section 2 defines HAD on panel OR repeated cross-section, but Phase 2a is panel-only. RCS inputs (disjoint unit IDs between periods) are rejected by the balanced-panel validator with the generic "unit(s) do not appear in both periods" error. A follow-up PR will add an RCS identification path based on pre/post cell means (rather than unit-level first differences), with its own validator and a distinct `data_mode` / API surface. | `diff_diff/had.py::_validate_had_panel`, `diff_diff/had.py::_aggregate_first_difference` | Phase 2a | Medium |
 
 #### Performance

diff_diff/__init__.py

@@ -57,6 +57,7 @@
 )
 from diff_diff.had import (
     HeterogeneousAdoptionDiD,
+    HeterogeneousAdoptionDiDEventStudyResults,
     HeterogeneousAdoptionDiDResults,
 )
 from diff_diff.estimators import (
@@ -436,9 +437,10 @@
     # Bias-corrected local-linear (Phase 1c for HeterogeneousAdoptionDiD)
     "BiasCorrectedFit",
     "bias_corrected_local_linear",
-    # HeterogeneousAdoptionDiD (Phase 2a)
+    # HeterogeneousAdoptionDiD (Phase 2a single-period, Phase 2b event study)
     "HeterogeneousAdoptionDiD",
     "HeterogeneousAdoptionDiDResults",
+    "HeterogeneousAdoptionDiDEventStudyResults",
     "HAD",
     # Datasets
     "load_card_krueger",