diff --git a/CHANGELOG.md b/CHANGELOG.md
index 39888e0d..896aecb9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`MultiPeriodDiD(absorb=..., vcov_type in {"hc2", "hc2_bm"})` now supported** (`diff_diff/estimators.py:1476`). Mirrors the DiD-absorb auto-route shipped earlier in this release: when `absorb=` is paired with `vcov_type in {"hc2","hc2_bm"}`, `MultiPeriodDiD.fit()` promotes the absorb columns to `fixed_effects=` internally so the existing full-dummy-design code path computes the algebraically correct vcov on the event-study design (`treated + period_X dummies + treated:period_X interactions + factor(unit)`). Verified at ~1e-10 vs `lm() + sandwich::vcovHC(type="HC2")` and `lm() + clubSandwich::vcovCR(cluster=1:n, type="CR2")` on a 5-cohort × 5-period event-study fixture (new `tests/test_estimators_vcov_type.py::TestMPDAbsorbedFERParity` against `benchmarks/data/clubsandwich_cr2_golden.json` scenario `mpd_absorbed_fe_did`). HC1/CR1 paths on `absorb=` are unchanged (no leverage term). `TwoWayFixedEffects(vcov_type in {"hc2","hc2_bm"})` rejection remains as a follow-up (different fit-path structure — no `fixed_effects=` equivalent inside TWFE). **Behavioral note (full `MultiPeriodDiDResults` surface change under auto-route):** under the auto-route, the entire returned `MultiPeriodDiDResults` reflects the full-dummy fit rather than the within-transformed fit — `result.coefficients`, `result.vcov`, `result.residuals`, `result.fitted_values`, `result.r_squared` all include the FE-dummy entries / un-demeaned values. `result.period_effects[t].effect` / `.se` / `.p_value` / `.conf_int` and `result.avg_att` / `.avg_se` are invariant to this routing (FWL guarantee). MPD requires a time-invariant ever-treated indicator that lies in the span of the intercept and the post-auto-route unit FE dummies (the exact alias depends on the omitted FE reference category under `pd.get_dummies(drop_first=True)`, not just on "the sum of treated-cohort unit dummies"), so `solve_ols` drops one column from that collinear set under R-style rank-deficiency handling. Which specific column is dropped is pivot-order and dummy-coding dependent (in the shipped parity fixture it is a never-treated unit dummy, not the `treated` main effect itself). The per-period interaction coefficients (`treated:period_X`) and `avg_att` are identified and invariant to that choice; parity tests target those rather than the `treated` main effect. **Survey-design scope (replicate weights):** when `survey_design=` uses replicate weights, the auto-route short-circuits the absorb-refit branch at `estimators.py:1693` and routes through the standard `compute_replicate_vcov` path on the fixed full-dummy design — correct because the design does not depend on replicate weights so no per-replicate refit is needed. **Redundant time-FE skip:** when the routed (or directly-supplied) `fixed_effects` list contains the `time` column, MPD silently skips emitting `<time>_<X>` dummies for that entry because the design already absorbs the time dimension via the non-reference period dummies; without the skip, the two blocks would collide on dummy names and the `coefficients` dict would silently collapse duplicates under `var_names`-keyed construction, breaking the coefficients-vs-vcov alignment that downstream consumers rely on. This applies to both the new `absorb=` auto-route and the pre-existing `fixed_effects=[<time_col>]` invocation.
 - **`DifferenceInDifferences(absorb=..., vcov_type in {"hc2", "hc2_bm"})` now supported** (`diff_diff/estimators.py:382`). Previously raised `NotImplementedError` because the HC2 leverage correction and CR2 Bell-McCaffrey DOF depend on the FULL FE hat matrix, while within-transformation (FWL) preserves coefficients and residuals but not the hat. Lift via internal auto-route: when `absorb=` is paired with `vcov_type in {"hc2","hc2_bm"}`, the fit promotes the absorb columns to `fixed_effects=` internally so the existing full-dummy-design code path computes the algebraically correct vcov. Empirically matches `lm() + sandwich::vcovHC(type="HC2")` and `lm() + clubSandwich::vcovCR(cluster=..., type="CR2")` at ~1e-10 (verified via new `tests/test_estimators_vcov_type.py::TestDiDAbsorbedFERParity` against `benchmarks/data/clubsandwich_cr2_golden.json` scenario `absorbed_fe_did`, with the R generator using the singleton-cluster CR2 trick for one-way HC2-BM Satterthwaite DOF). HC1/CR1 paths unchanged. `MultiPeriodDiD(absorb=...)` and `TwoWayFixedEffects` rejections remain as follow-ups (different fit-path structure). **Behavioral note (full `DiDResults` surface change under auto-route):** under the auto-route, the entire returned `DiDResults` reflects the full-dummy fit rather than the within-transformed fit. Specifically, `result.coefficients` and `result.vcov` include the FE-dummy entries (matching the `fixed_effects=` path), `result.residuals` and `result.fitted_values` are on the un-demeaned outcome scale, and `result.r_squared` is computed on the un-demeaned outcome (so it absorbs the FE variance and will typically be higher than the within-R²). `result.att` is invariant to this routing (FWL guarantee). Downstream consumers reading `result.att` are unaffected; consumers reading the broader result surface should expect the full-dummy values. **Survey-design scope:** the auto-route changes the FE handling (and removes the prior absorbed-FE rejection), but `survey_design=` continues to drive its own variance path (Taylor-series linearization or replicate-weight variance, per the existing survey contract) rather than the analytical HC2/HC2-BM sandwich. The auto-route is therefore methodologically meaningful for non-survey fits and for the FE-handling side of survey fits; analytical small-sample inference under `vcov_type in {"hc2","hc2_bm"}` is bypassed when a survey design is supplied.
 - **BaconDecomposition R parity goldens.** Closes the PR-B deferral row in `TODO.md`. JSON goldens at `benchmarks/data/r_bacondecomp_golden.json` generated from the committed `benchmarks/R/generate_bacon_golden.R` script (3 fixtures: `uniform_3groups_with_never_treated`, `two_groups_no_never_treated`, `always_treated_remapped`) against `bacondecomp 0.1.1` on R 4.5.2. `tests/test_methodology_bacon.py::TestBaconParityR` now active (4 tests, no skips): TWFE coefficient parity at `atol=1e-6` across all 3 fixtures; weights-sum parity at `atol=1e-6` across all 3 fixtures; per-component estimate + weight parity at `atol=1e-6` on the 2 non-remap fixtures **and on the 6 timing-vs-timing rows of `always_treated_remapped`** (carve-out narrowed to U-bucket rows only); plus a dedicated fold-back test (`test_always_treated_remapped_fold_back_matches_r`) that pins the **documented convention divergence** on `always_treated_remapped` (R keeps `first_treat=1` as a distinct timing cohort and emits `Later vs Always Treated` comparisons; Python's paper-footnote-11 convention remaps those units to `U` and folds them into a single `treated_vs_never` cell per treated cohort) by aggregating R's split rows per cohort and asserting they match Python's single fold at `atol=1e-6`. The aggregate is invariant per Theorem 1; the per-component breakdown differs structurally between conventions but the fold-back is now directly asserted. New `**Note (R parity convention divergence on always-treated)**` and `**Deviation (first-period boundary extension on always-treated remap)**` in `docs/methodology/REGISTRY.md`. **First-period boundary deviation:** the paper uses strict `t_i < 1` for the always-treated bucket; the library uses the inclusive `first_treat <= min(time)` rule and folds `first_treat == min(time)` cohorts into `U`. R does NOT apply this fold (it keeps such cohorts as their own bucket). When `min(time) > 1` the rules coincide. Explicitly labeled in REGISTRY's Deviations block and mirrored in `METHODOLOGY_REVIEW.md` and `bacon.py`. METHODOLOGY_REVIEW.md tracker row promoted `**Complete** (R parity goldens pending)` → `**Complete**`.
 - **`generate_ddd_panel_data` — panel-structured DGP for Triple-Difference power analysis** (`diff_diff/prep_dgp.py`). New public function exported from `diff_diff` and `diff_diff.prep` for panel DDD simulations. Cross-sectional `generate_ddd_data` remains available unchanged. Produces a balanced panel of `n_units × n_periods` with two unit-level binary dimensions (`group`, `partition`) and a derived `post = 1[period >= treatment_period]` indicator; columns: `unit, period, outcome, group, partition, post, treated, true_effect` (+ `x1, x2` when `add_covariates=True`). DDD-CPT identification holds because the `group * partition` interaction enters as a unit-level (time-invariant) term, leaving the triple-interaction `treatment_effect * group * partition * post` as the sole source of differential group × partition trend. Compatible with `TripleDifference(cluster="unit").fit(..., time="post")` (the cluster kwarg is required because `TripleDifference` is the repeated-cross-section `panel=FALSE` estimator and unclustered SE on panel-generated rows understates variance under within-unit serial correlation; the point estimate `att` is invariant to clustering — see the new `TripleDifference` REGISTRY note on panel-shaped input). Users get panel-realistic unit fixed effects and within-unit serial correlation while the binary 2×2×2 estimator surface is unchanged. **Stratified allocation:** the partition split is drawn stratified-by-group at the requested `partition_frac` so every `(group, partition)` cell receives at least one unit; a targeted `ValueError` is raised at fit-time when the rounded cell counts (`n_units`, `group_frac`, `partition_frac`) would leave any cell empty. This guarantees the 2x2x2 DDD surface is populated for any valid input — independent marginal sampling (the cross-sectional `generate_ddd_data` convention) could collapse cells when marginals are small (e.g., `n_units=4, group_frac=partition_frac=0.25`). Validates `1 <= treatment_period < n_periods`, `group_frac` and `partition_frac` strictly in `(0, 1)`, and `n_units >= 4`. Deterministic recovery (`noise_sd=0`) matches `treatment_effect` to ~1e-15 (covered by `tests/test_prep.py::TestGenerateDddPanelData`, 16 tests including infeasible-config rejection and smallest-feasible-config round-trip through `TripleDifference.fit`). `power.simulate_power` is NOT yet auto-routed to the panel DGP for `TripleDifference` (the existing `_ddd_dgp_kwargs` registry entry still ignores `n_periods` and the existing `_check_ddd_dgp_compat` warning still fires on non-default kwargs) — that wiring is tracked as a follow-up in TODO.md.
diff --git a/TODO.md b/TODO.md
index b1079ad3..667ff7bd 100644
--- a/TODO.md
+++ b/TODO.md
@@ -96,7 +96,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | WooldridgeDiD: Stata `jwdid` golden value tests — add R/Stata reference script and `TestReferenceValues` class. | `tests/test_wooldridge.py` | #216 | Medium |
 | Thread `vcov_type` (classical / hc1 / hc2 / hc2_bm) through the 8 standalone estimators that expose `cluster=`: `CallawaySantAnna`, `SunAbraham`, `ImputationDiD`, `TwoStageDiD`, `TripleDifference`, `StackedDiD`, `WooldridgeDiD`, `EfficientDiD`. Phase 1a added `vcov_type` to the `DifferenceInDifferences` inheritance chain only. | multiple | Phase 1a | Medium |
 | Weighted one-way Bell-McCaffrey (`vcov_type="hc2_bm"` + `weights`, no cluster) currently raises `NotImplementedError`. `_compute_bm_dof_from_contrasts` builds its hat matrix from the unscaled design via `X (X'WX)^{-1} X' W`, but `solve_ols` solves the WLS problem by transforming to `X* = sqrt(w) X`, so the correct symmetric idempotent residual-maker is `M* = I - sqrt(W) X (X'WX)^{-1} X' sqrt(W)`. Rederive the Satterthwaite `(tr G)^2 / tr(G^2)` ratio on the transformed design and add weighted parity tests before lifting the guard. | `linalg.py::_compute_bm_dof_from_contrasts`, `linalg.py::_validate_vcov_args` | Phase 1a | Medium |
-| HC2 / HC2 + Bell-McCaffrey on absorbed-FE fits — REMAINING sub-gates: `TwoWayFixedEffects` (`twfe.py:154` rejects unconditionally); `MultiPeriodDiD(absorb=..., vcov_type in {"hc2","hc2_bm"})` (`estimators.py:1458` rejects). The DiD sub-gate (`DifferenceInDifferences(absorb=..., vcov_type in {"hc2","hc2_bm"})`) was lifted via auto-route to `fixed_effects=` internally; clubSandwich-parity at 1e-10 verified. The same auto-route pattern can apply to MPD-absorb; TWFE is its own class and may need different surgery (TWFE always within-transforms with no equivalent `fixed_effects=` path). Within-transformation preserves coefficients and residuals under FWL but not the hat matrix; HC1/CR1 are unaffected (no leverage term). | `twfe.py::fit`, `estimators.py::MultiPeriodDiD.fit` | follow-up | Medium |
+| HC2 / HC2 + Bell-McCaffrey on absorbed-FE fits — REMAINING sub-gate: `TwoWayFixedEffects` (`twfe.py:154` rejects unconditionally). The DiD sub-gate and the MultiPeriodDiD sub-gate were both lifted via auto-route to `fixed_effects=` internally (DiD: PR #458, ~1e-10 vs clubSandwich; MPD: this release, ~1e-10 vs sandwich::vcovHC and clubSandwich::vcovCR). TWFE has no equivalent `fixed_effects=` code path (always within-transforms), so the same auto-route surgery is not directly applicable — lifting requires either building the full-dummy design inline or refactoring TWFE to delegate to DiD. Within-transformation preserves coefficients and residuals under FWL but not the hat matrix; HC1/CR1 are unaffected (no leverage term). | `twfe.py::fit` | follow-up | Medium |
 | Weighted CR2 Bell-McCaffrey cluster-robust (`vcov_type="hc2_bm"` + `cluster_ids` + `weights`) currently raises `NotImplementedError`. Weighted hat matrix and residual rebalancing need threading per clubSandwich WLS handling. | `linalg.py::_compute_cr2_bm` | Phase 1a | Medium |
 | Unify Rust local-method `estimate_model` solver path to `solve_wls_svd` (the same SVD helper used by the global-method since PR #348) for sub-1e-14 bootstrap SE parity. Current local-method bootstrap parity test (`tests/test_rust_backend.py::TestTROPRustEdgeCaseParity::test_bootstrap_seed_reproducibility_local`) passes at `atol=1e-5` — the residual ~1e-7 gap is roundoff between Rust's `estimate_model` matrix factorization and numpy's `lstsq`, which accumulates differently across per-replicate bootstrap fits. Main-fit ATT parity is regime-dependent (`atol=1e-14` for `lambda_nn=inf`, `atol=1e-10` for finite `lambda_nn` — see `test_local_method_main_fit_parity`); the bootstrap gap is a same-solver-path roundoff concern and not a user-visible correctness bug. | `rust/src/trop.rs::estimate_model`, `rust/src/linalg.rs::solve_wls_svd` | follow-up | Low |
 | Rust multiplier-bootstrap weight RNG (`generate_bootstrap_weights_batch` in `rust/src/bootstrap.rs:9-10, 57-75`) uses `Xoshiro256PlusPlus::seed_from_u64(seed + i)` per row for Rademacher/Mammen/Webb generation. If any Python caller (SDID / efficient-DiD multiplier bootstrap) has a numpy-canonical equivalent, the two backends likely diverge under the same seed. Audit Python callers (`diff_diff/sdid.py`, `diff_diff/efficient_did_bootstrap.py`, `diff_diff/bootstrap_utils.py::generate_bootstrap_weights_batch_numpy`) for parity-test gaps. Same fix shape as TROP RNG parity (PR #354): pre-generate weights in Python via numpy and pass them to Rust through PyO3. | `rust/src/bootstrap.rs`, `diff_diff/bootstrap_utils.py` | follow-up | Medium |
diff --git a/benchmarks/R/generate_clubsandwich_golden.R b/benchmarks/R/generate_clubsandwich_golden.R
index a4e748cb..cf11f957 100644
--- a/benchmarks/R/generate_clubsandwich_golden.R
+++ b/benchmarks/R/generate_clubsandwich_golden.R
@@ -122,6 +122,56 @@ output$absorbed_fe_did <- list(
   dof_cr2 = as.numeric(ct_did_cr2$df_Satt)
 )
 
+# --- Absorbed-FE MultiPeriodDiD event-study scenario (gate lift PR) ----------
+# Mirrors MPD(fixed_effects=["unit"]) destination of the absorb auto-route on
+# MultiPeriodDiD. MPD parameterization: const + treated + period_f (non-ref)
+# + treated:period_X (non-ref) + factor(unit). Build the interaction columns
+# explicitly so the R fit's coefficient names match MPD's `treated:period_X`.
+
+make_mpd_panel <- function(n_total, units_per_cohort, n_periods, seed) {
+  set.seed(seed)
+  d <- expand.grid(unit = seq_len(n_total), period = seq_len(n_periods))
+  d$cohort <- ((d$unit - 1L) %/% units_per_cohort) + 1L
+  n_cohorts <- n_total %/% units_per_cohort
+  # Last cohort is never-treated control; preceding cohorts ever-treated.
+  d$treated <- as.integer(d$cohort < n_cohorts)
+  d$y <- 1 + 0.5 * d$treated * (d$period >= 3) +
+         rnorm(nrow(d), sd = 0.5) +
+         0.1 * d$unit + 0.2 * d$period
+  d
+}
+
+d_mpd <- make_mpd_panel(n_total = 25, units_per_cohort = 5, n_periods = 5,
+                        seed = 12345)
+d_mpd$period_f <- relevel(factor(d_mpd$period), ref = "1")
+# Explicit interaction columns to match MPD's parameterization exactly.
+for (p in 2:5) {
+  d_mpd[[paste0("treated_period_", p)]] <- d_mpd$treated * (d_mpd$period == p)
+}
+fit_mpd <- lm(y ~ treated + period_f +
+                  treated_period_2 + treated_period_3 +
+                  treated_period_4 + treated_period_5 +
+                  factor(unit),
+              data = d_mpd)
+vcov_mpd_hc2 <- sandwich::vcovHC(fit_mpd, type = "HC2")
+vcov_mpd_hc2_bm <- vcovCR(fit_mpd, cluster = seq_len(nrow(d_mpd)), type = "CR2")
+ct_mpd_hc2_bm <- coef_test(fit_mpd, vcov = vcov_mpd_hc2_bm)
+output$mpd_absorbed_fe_did <- list(
+  unit = d_mpd$unit,
+  period = d_mpd$period,
+  treated = d_mpd$treated,
+  y = d_mpd$y,
+  coef = as.numeric(coef(fit_mpd)),
+  coef_names = names(coef(fit_mpd)),
+  vcov_hc2 = as.numeric(vcov_mpd_hc2),
+  vcov_hc2_shape = dim(vcov_mpd_hc2),
+  vcov_hc2_bm = as.numeric(vcov_mpd_hc2_bm),
+  vcov_hc2_bm_shape = dim(vcov_mpd_hc2_bm),
+  dof_hc2_bm = as.numeric(ct_mpd_hc2_bm$df_Satt),
+  reference_period = 1L,
+  target_period = 4L
+)
+
 output$meta <- list(
   source = "clubSandwich",
   clubSandwich_version = as.character(packageVersion("clubSandwich")),
diff --git a/benchmarks/data/clubsandwich_cr2_golden.json b/benchmarks/data/clubsandwich_cr2_golden.json
index e7f453e7..3524bb56 100644
--- a/benchmarks/data/clubsandwich_cr2_golden.json
+++ b/benchmarks/data/clubsandwich_cr2_golden.json
@@ -49,11 +49,26 @@
     "vcov_cr2_shape": [12, 12],
     "dof_cr2": [3.000000000000001, 6.000000000000003, 1.373392274582593, 1.945786883798724, 1.867171596521997, 6.000000000000002, 6.000000000000001, 6.000000000000006, 6.000000000000002, 3.77697841726619, 3.776978417266191, 3.776978417266191]
   },
+  "mpd_absorbed_fe_did": {
+    "unit": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
+    "period": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+    "treated": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+    "y": [1.592764408921928, 1.754733008754762, 1.445348342659473, 1.373251413268618, 2.002943727920197, 0.8910220161481357, 2.215049275534196, 1.861907947387392, 1.957920128028314, 1.740338998762936, 2.241876096823999, 3.308656021852109, 2.685313932128977, 2.860108228777479, 2.324734002748834, 3.208449919760292, 2.456821239378394, 2.834211205028724, 3.660356325834778, 3.349361849633647, 3.689810962277662, 4.127892541238432, 3.17783578538435, 2.823431297385157, 2.901145241651844, 2.402548759405412, 1.359176318152682, 2.010189900649211, 2.106061746325425, 1.818844511540937, 2.40593658927693, 3.198416773173767, 3.224595168703096, 3.116222819740233, 2.527135596407027, 2.74559413963628, 2.437956710631441, 1.868974878070683, 3.683866925436484, 2.912900524323904, 3.564255416795089, 1.909820969301479, 2.669867223923734, 3.768570270091454, 3.827225860165277, 4.230364701552045, 2.893450611055403, 3.983701626712241, 4.091593826717843, 3.246600583267788, 1.929806963068948, 3.27384633236602, 2.426795135178727, 2.675831420277619, 2.264511730503031, 2.838976847403072, 3.145585636437442, 3.311897664353043, 4.072532510240145, 1.926528010818313, 3.274795990307604, 2.628734259135228, 3.67665153775195, 4.294981421347705, 3.30656020280645, 2.783811346895301, 4.24406971632601, 4.696744236178137, 4.258427335331193, 3.452164159852588, 3.727307787664688, 3.407675313404228, 3.375323590521867, 5.165255981202357, 4.801352691433715, 2.871300425345763, 2.913129143576215, 2.194229754904459, 2.938124140376827, 3.310629203740546, 3.222691537103039, 3.52157177577572, 2.94781544365317, 4.438555458481485, 3.785610336518722, 4.333549592283361, 3.836021234507394, 3.446023309581331, 3.968261858355437, 4.212435032675837, 3.418049260493099, 3.572458744450104, 5.043473471012831, 4.004090314002409, 3.809683525525007, 4.243666050278521, 3.747478241239552, 5.178859908253208, 3.900101218087583, 3.952726653364625, 2.711962703760644, 2.121888334879613, 3.0112092638935, 2.237622372075537, 3.070542156430142, 2.831976000754527, 3.044196957710926, 4.078054821300628, 3.175983354352765, 3.660561768746984, 2.984913876559905, 3.037970653922923, 4.430621137194148, 4.559615859821168, 3.959623119315983, 3.847455096864254, 4.173923203334838, 4.614430312554218, 5.490001198742418, 4.465491346740392, 4.872431801018798, 4.860726008581316, 4.461075787681401, 5.165477559867579, 4.289380153330713],
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+    "vcov_hc2_bm_shape": [33, 33],
+    "dof_hc2_bm": [4.853932584269628, 9.64773087908587, 7.529411764705872, 7.529411764705888, 7.529411764705889, 7.529411764705892, 11.611917494270315, 11.611917494270454, 11.611917494270452, 11.611917494270408, 7.977900552486196, 7.977900552486197, 7.977900552486195, 7.977900552486195, 7.977900552486197, 7.977900552486192, 7.977900552486195, 7.977900552486199, 7.977900552486192, 7.977900552486197, 7.977900552486199, 7.977900552486194, 7.977900552486194, 7.977900552486194, 7.977900552486196, 7.977900552486194, 7.977900552486197, 7.977900552486196, 7.977900552486194, 7.529411764705895, 7.529411764705896, 7.529411764705891, 7.529411764705896],
+    "reference_period": 1,
+    "target_period": 4
+  },
   "meta": {
     "source": "clubSandwich",
     "clubSandwich_version": "0.7.0",
     "R_version": "R version 4.5.2 (2025-10-31)",
-    "generated_at": "2026-05-16 21:17:21 UTC",
+    "generated_at": "2026-05-17 10:36:19 UTC",
     "note": "CR2 Bell-McCaffrey cluster-robust parity target for diff_diff._compute_cr2_bm"
   }
 }
diff --git a/diff_diff/estimators.py b/diff_diff/estimators.py
index 84e712cf..9d73e968 100644
--- a/diff_diff/estimators.py
+++ b/diff_diff/estimators.py
@@ -1449,16 +1449,9 @@ def fit(  # type: ignore[override]
         n_treated_raw = int(np.sum(data[treatment].values.astype(float)))
         n_control_raw = len(data) - n_treated_raw
 
-        # Reject multi-absorb with survey weights (single-pass demeaning is
-        # not the correct weighted FWL projection for N > 1 dimensions)
-        if absorb and len(absorb) > 1 and survey_weights is not None:
-            raise ValueError(
-                f"Multiple absorbed fixed effects (absorb={absorb}) with survey "
-                "weights is not supported. Single-pass sequential demeaning is not "
-                "the correct weighted FWL projection for multiple absorbed dimensions. "
-                "Use absorb with a single variable, or use fixed_effects= instead."
-            )
-
+        # Mutual-exclusion check runs ABOVE the auto-route below so that the
+        # `absorb=..., fixed_effects=...` combination still rejects rather
+        # than being silently merged.
         if absorb and fixed_effects:
             raise ValueError(
                 "Cannot use both absorb and fixed_effects. "
@@ -1468,19 +1461,50 @@ def fit(  # type: ignore[override]
                 "or fixed_effects alone (for low-dimensional FE)."
             )
 
-        # Reject HC2 / HC2 + Bell-McCaffrey on absorbed-FE fits (see the
-        # matching guard in DifferenceInDifferences.fit / twfe.py for the
-        # methodology reasoning: HC2/CR2 leverage corrections depend on the
-        # full FE hat matrix, not the residualized design from within-
-        # transformation). Tracked in TODO.md.
+        # Auto-route absorb → fixed_effects when vcov_type needs the FULL FE
+        # hat matrix. Mirrors the identical pattern in
+        # DifferenceInDifferences.fit (PR #458). HC2 leverage and CR2
+        # Bell-McCaffrey DOF both depend on the full-design hat; FWL
+        # preserves coefficients and residuals but not the hat matrix, so
+        # the demeaned design's leverage is wrong for these vcov families.
+        # Building the full-dummy design and routing through the existing
+        # fixed_effects= branch produces the algebraically correct vcov.
+        # Empirically matches `lm() + sandwich::vcovHC` and
+        # `lm() + clubSandwich::vcovCR` (singleton-cluster trick for one-way
+        # HC2-BM; PT2018 §3.3 unweighted CR2 algebra) at ~1e-15.
+        # Conley vcov is unaffected: the absorb+Conley path computes the
+        # panel sandwich on demeaned scores, which is FWL-correct because
+        # Conley's meat uses only residuals (no leverage term).
+        # HC1/CR1 paths remain on the demeaned design (no leverage term).
+        #
+        # Survey-replicate scope: this also short-circuits the absorb-refit
+        # replicate-variance branch below (search "compute_replicate_refit_variance").
+        # Correct: with a fixed full-dummy design, replicate variance doesn't
+        # need per-replicate refit — the standard compute_replicate_vcov
+        # path applies directly because the design matrix does not depend
+        # on the replicate weights.
+        #
+        # Placement: this auto-route runs BEFORE the multi-absorb +
+        # survey-weights guard because that guard's rationale ("single-pass
+        # demeaning is not the correct weighted FWL projection for N > 1
+        # dimensions") doesn't apply when we're about to swap absorb for
+        # fixed_effects: the fixed_effects= path builds the full-dummy
+        # design and solves WLS directly, with no within-transform step.
         if absorb and self.vcov_type in ("hc2", "hc2_bm"):
-            raise NotImplementedError(
-                f"MultiPeriodDiD(absorb=..., vcov_type={self.vcov_type!r}) "
-                "is not yet supported: absorbed fixed effects are handled "
-                "by demeaning, and the HC2 / CR2 Bell-McCaffrey leverage "
-                "corrections depend on the full FE hat matrix, not the "
-                "residualized one. Use vcov_type='hc1' with absorb=, or "
-                "switch to fixed_effects= dummies for a full-dummy design."
+            fixed_effects = list(fixed_effects or []) + list(absorb)
+            absorb = None
+            n_absorbed_effects = 0
+
+        # Reject multi-absorb with survey weights (single-pass demeaning is
+        # not the correct weighted FWL projection for N > 1 dimensions).
+        # Only fires when absorb is still set — i.e., the auto-route above
+        # didn't consume it.
+        if absorb and len(absorb) > 1 and survey_weights is not None:
+            raise ValueError(
+                f"Multiple absorbed fixed effects (absorb={absorb}) with survey "
+                "weights is not supported. Single-pass sequential demeaning is not "
+                "the correct weighted FWL projection for multiple absorbed dimensions. "
+                "Use absorb with a single variable, or use fixed_effects= instead."
             )
 
         # MultiPeriodDiD is intrinsically a multi-period panel estimator;
@@ -1576,9 +1600,22 @@ def fit(  # type: ignore[override]
                 X = np.column_stack([X, working_data[cov].values.astype(float)])
                 var_names.append(cov)
 
-        # Add fixed effects as dummy variables
+        # Add fixed effects as dummy variables.
+        #
+        # MPD's design already absorbs the time dimension via non-reference
+        # period dummies (the `period_<X>` columns above) and the treatment-
+        # period interactions. If the caller passes the same column as a
+        # fixed effect (either explicitly or via the absorb -> fixed_effects
+        # auto-route for HC2/HC2-BM), the resulting `<time>_<X>` dummies
+        # would be perfectly redundant with the existing period dummies,
+        # NaN'd by `solve_ols`'s rank-deficiency handling, AND collide on
+        # name with the event-study columns in `coef_dict` (silently
+        # collapsing the dict and breaking the coefficients-vs-vcov
+        # alignment that downstream consumers rely on). Skip those FEs.
         if fixed_effects:
             for fe in fixed_effects:
+                if fe == time:
+                    continue
                 dummies = pd.get_dummies(working_data[fe], prefix=fe, drop_first=True)
                 for col in dummies.columns:
                     X = np.column_stack([X, dummies[col].values.astype(float)])
diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md
index 478716b8..6463463a 100644
--- a/docs/methodology/REGISTRY.md
+++ b/docs/methodology/REGISTRY.md
@@ -2550,8 +2550,8 @@ Shipped in `diff_diff/had_pretests.py` as `stute_joint_pretest()` (residuals-in
 - [x] Phase 1a: HC2 + Bell-McCaffrey DOF correction in `diff_diff/linalg.py` via `vcov_type="hc2_bm"` enum (both one-way and CR2 cluster-robust with Imbens-Kolesar / Pustejovsky-Tipton Satterthwaite DOF). Weighted cluster CR2 raises `NotImplementedError` and is tracked as Phase 2+ in `TODO.md`.
     - **Note (scope limitation on absorbed FE):** HC2 and HC2 + Bell-McCaffrey on within-transformed designs still depend on the FULL FE hat matrix because FWL preserves coefficients and residuals but NOT the hat matrix: `h_ii = x_i' (X'X)^{-1} x_i` on the reduced design is not the diagonal of the full FE projection, and CR2's block adjustment `A_g = (I - H_gg)^{-1/2}` likewise depends on the full cluster-block hat matrix. The status across the three estimators that previously rejected this combination:
         - **`DifferenceInDifferences(absorb=..., vcov_type in {"hc2","hc2_bm"})` — SUPPORTED (auto-route).** When the user pairs `absorb=` with HC2 / HC2-BM, `DiD.fit()` internally promotes the absorb columns to `fixed_effects=` so the existing full-dummy code path computes the algebraically correct vcov from the full FE projection. Verified at ~1e-10 vs `lm() + sandwich::vcovHC(type="HC2")` and `lm() + clubSandwich::vcovCR(cluster=..., type="CR2")` (singleton-cluster CR2 trick for one-way HC2-BM Satterthwaite DOF; PT2018 §3.3 unweighted CR2 algebra). **User-visible surface change**: under the auto-route, the entire `DiDResults` (coefficients, vcov, residuals, fitted_values, r_squared) reflect the full-dummy fit rather than the within-transformed fit — the FE-dummy entries are included in `result.coefficients` / `result.vcov`, `r_squared` is computed on the un-demeaned outcome, and `residuals` / `fitted_values` are on the original scale. `result.att` is unaffected (FWL-equivalent). HC1/CR1 paths on `absorb=` are unchanged (no leverage term). **Survey-design scope**: when `survey_design=` is supplied, the existing survey variance path (Taylor-series linearization / replicate weights) takes precedence over the analytical HC2/HC2-BM sandwich; the auto-route only changes the FE handling (removing the prior reject) and does not redirect to the analytical small-sample sandwich on survey fits.
-        - **`TwoWayFixedEffects(vcov_type in {"hc2","hc2_bm"})` — still rejects.** TWFE is a standalone class with no `fixed_effects=` equivalent path, so the same auto-route surgery would require building the full-dummy design inline. Tracked as a follow-up in `TODO.md`.
-        - **`MultiPeriodDiD(absorb=..., vcov_type in {"hc2","hc2_bm"})` — still rejects.** Same algebraic situation as DiD, but the `MultiPeriodDiD.fit()` body has many additional branches (cohort, event-study, survey, etc.) that complicate the auto-route surgery. Tracked as a follow-up in `TODO.md`.
+        - **`TwoWayFixedEffects(vcov_type in {"hc2","hc2_bm"})` — still rejects.** TWFE is a standalone class with no `fixed_effects=` equivalent path, so the same auto-route surgery used for DiD-absorb and MPD-absorb is not directly applicable; lifting requires building the full-dummy design inline or refactoring TWFE to delegate to DiD. Tracked as a follow-up in `TODO.md`.
+        - **`MultiPeriodDiD(absorb=..., vcov_type in {"hc2","hc2_bm"})` — SUPPORTED (auto-route).** Same auto-route pattern as `DifferenceInDifferences`: `MultiPeriodDiD.fit()` internally promotes the absorb columns to `fixed_effects=` for HC2 / HC2-BM callers, so the existing full-dummy code path computes the algebraically correct vcov from the full FE projection on the event-study design (`treated + period_X dummies + treated:period_X interactions + factor(unit)`). Verified at ~1e-10 vs `lm() + sandwich::vcovHC(type="HC2")` and `lm() + clubSandwich::vcovCR(cluster=1:n, type="CR2")` on a 5-cohort × 5-period event-study fixture; the parity target is a per-period interaction `treated:period_X` because MPD requires the `treated` column to be a time-invariant ever-treated indicator, which lies in the span of the intercept and the post-auto-route unit FE dummies (under `pd.get_dummies(..., drop_first=True)` the dropped reference unit is implicit in the intercept, so the exact alias relation depends on the omitted FE category — it is NOT simply "the sum of treated-cohort unit dummies"). `solve_ols` drops one column from the collinear set under R-style rank-deficiency handling; in the shipped parity fixture (4 ever-treated cohorts of 5 units + 1 never-treated cohort of 5 units) it drops a unit dummy from the never-treated cohort (`unit_25`) and the `treated` main effect remains finite, but the specific column that gets NaN'd is pivot-order and dummy-coding dependent. Either way, the slope coefficients (`treated:period_X`) and the post-period-average `avg_att` are identified and invariant to which column was dropped. Same `MultiPeriodDiDResults` surface change as DiD: `vcov`, `residuals`, `fitted_values`, `r_squared`, and `coefficients` reflect the full-dummy fit, with `period_effects[t].effect` / `.se` / `.p_value` / `.conf_int` invariant by FWL. HC1/CR1 paths on `absorb=` are unchanged (no leverage term). Same survey-design scope as DiD: replicate-weight variance routes through the standard `compute_replicate_vcov` path on the fixed full-dummy design rather than the per-replicate refit branch (which targets the demeaning path); since the auto-routed design does not depend on replicate weights, no refit is needed. **Redundant time-FE skip:** when the routed (or directly-supplied) `fixed_effects` list contains the `time` column, MPD silently skips emitting `<time>_<X>` dummies for that entry because the design already absorbs time via the non-reference period dummies. Without the skip, those blocks collide on dummy names and `MultiPeriodDiDResults.coefficients` (built as `{name: coef for name, coef in zip(var_names, coefficients)}`) would silently drop duplicates, breaking the coefficients-vs-vcov alignment that downstream consumers (HonestDiD sub-VCV extraction, BusinessReport, etc.) rely on. The skip applies to BOTH the new `absorb=` auto-route AND the pre-existing `fixed_effects=[<time_col>]` invocation (pre-PR, `fixed_effects=["unit", time]` produced a dict with `len < vcov.shape[0]` and NaN values overwriting the real event-study period coefficients).
         - Workarounds for the still-rejecting paths: use `vcov_type="hc1"` (HC1/CR1 have no leverage term and survive FWL), or switch to `fixed_effects=` dummies so the hat matrix is computed on the full design.
 - [x] Phase 1a: `vcov_type` enum threaded through `DifferenceInDifferences` (`MultiPeriodDiD`, `TwoWayFixedEffects` inherit); `robust=True` <=> `vcov_type="hc1"`, `robust=False` <=> `vcov_type="classical"`. Conflict detection at `__init__`. Results summary prints the variance-family label.
     - **Note (deviation from the fully-symmetric enum):** `MultiPeriodDiD(cluster=..., vcov_type="hc2_bm")` is intentionally **not supported** and raises `NotImplementedError`. The scalar-coefficient `DifferenceInDifferences` path handles the cluster + CR2 Bell-McCaffrey combination (`_compute_cr2_bm` returns a per-coefficient Satterthwaite DOF that is valid for the single-ATT contrast), but `MultiPeriodDiD` also reports a post-period-average ATT constructed as a *contrast* of the event-study coefficients. The cluster-aware CR2 BM DOF for that contrast (i.e., the Pustejovsky-Tipton 2018 per-cluster adjustment matrices applied to an arbitrary aggregation contrast) is not yet implemented. Pairing CR2 cluster-robust SEs with the one-way Imbens-Kolesar (2016) contrast DOF would be a broken hybrid, so the combination fails fast with a clear workaround message (drop the cluster for one-way HC2+BM, or use `vcov_type="hc1"` with cluster for CR1 Liang-Zeger). Tracked in `TODO.md` under Methodology/Correctness. Applies only to `MultiPeriodDiD`; `DifferenceInDifferences(cluster=..., vcov_type="hc2_bm")` works.
diff --git a/tests/test_estimators_vcov_type.py b/tests/test_estimators_vcov_type.py
index 2f346049..8ea85b62 100644
--- a/tests/test_estimators_vcov_type.py
+++ b/tests/test_estimators_vcov_type.py
@@ -744,33 +744,6 @@ def test_did_fixed_effects_dummies_still_accept_hc2_and_hc2_bm(self):
             assert np.isfinite(res.att)
             assert np.isfinite(res.se)
 
-    def test_multi_period_absorb_rejects_hc2_and_hc2_bm(self):
-        """MultiPeriodDiD with absorb= rejects HC2/HC2+BM for the same
-        methodology reason as the base class."""
-        rng = np.random.default_rng(20260420)
-        n_units, n_time = 30, 4
-        rows = []
-        for i in range(n_units):
-            treated = int(i >= n_units // 2)
-            for t in range(n_time):
-                y = rng.normal(0.0, 1.0) + 0.3 * treated + 0.5 * treated * (t >= 2)
-                rows.append({"unit": i, "time": t, "treated": treated, "y": y})
-        data = pd.DataFrame(rows)
-
-        for bad in ("hc2", "hc2_bm"):
-            with pytest.raises(
-                NotImplementedError,
-                match="MultiPeriodDiD.*absorb.*not yet supported",
-            ):
-                MultiPeriodDiD(vcov_type=bad).fit(
-                    data,
-                    outcome="y",
-                    treatment="treated",
-                    time="time",
-                    absorb=["unit"],
-                    unit="unit",
-                )
-
     def test_summary_suppresses_variance_line_under_wild_bootstrap(self):
         """When inference_method='wild_bootstrap', the Variance label is omitted.
 
@@ -1217,3 +1190,430 @@ def test_absorb_hc2_bm_df_sensitive_inference(self):
             f"HC2 CI width ({width_hc2:.6f}) — BM Satterthwaite DOF is "
             "smaller than n-k, so the critical value is larger."
         )
+
+
+class TestMPDAbsorbedFERParity:
+    """R-parity for `MultiPeriodDiD(absorb=..., vcov_type in {hc2, hc2_bm})`.
+
+    Mirrors `TestDiDAbsorbedFERParity`. The auto-route promotes `absorb=` to
+    `fixed_effects=` internally; MPD's existing `fixed_effects=` code path
+    builds the full-dummy design that R's `lm()` produces.
+
+    Collinearity note: MPD's `treated` is a time-invariant ever-treated
+    indicator, so it lies in the span of the intercept and the
+    post-auto-route unit FE dummies (under `pd.get_dummies(drop_first=True)`
+    the dropped reference unit is folded into the intercept; the exact
+    alias relation depends on the omitted category and is NOT simply
+    "the sum of treated-cohort unit dummies"). `solve_ols` resolves this
+    by dropping one column from the collinear set under R-style
+    rank-deficiency handling. In the shipped parity fixture the dropped
+    column is a unit dummy from the never-treated cohort (`unit_25`) and
+    the `treated` main effect remains finite, but the specific column
+    dropped is pivot-order and dummy-coding dependent. Tests therefore
+    pin parity on a per-period interaction (`treated:period_4`) which is
+    identified independent of that choice, exposed as
+    `result.period_effects[4]`.
+
+    Time-FE skip note: when the routed (or explicit) `fixed_effects` list
+    contains the `time` column, MPD silently skips emitting `<time>_<X>`
+    dummies for that entry because the design already absorbs the time
+    dimension via the non-reference period dummies. The
+    `test_absorb_hc2_result_surface_invariants_multi_absorb` test pins
+    that the resulting `coefficients` dict cardinality matches `vcov`
+    rank and has no duplicate names.
+    """
+
+    def _load_golden(self):
+        import json
+        from pathlib import Path
+
+        golden_path = (
+            Path(__file__).parent.parent / "benchmarks" / "data" / "clubsandwich_cr2_golden.json"
+        )
+        if not golden_path.exists():
+            pytest.skip(
+                "Golden JSON not present; run `Rscript "
+                "benchmarks/R/generate_clubsandwich_golden.R` to generate."
+            )
+        with open(golden_path) as f:
+            golden = json.load(f)
+        if "mpd_absorbed_fe_did" not in golden:
+            pytest.skip(
+                "Golden JSON does not yet include `mpd_absorbed_fe_did` scenario; "
+                "regenerate via the R script."
+            )
+        return golden["mpd_absorbed_fe_did"]
+
+    def _make_data(self, d):
+        return pd.DataFrame(
+            {
+                "unit": d["unit"],
+                "period": d["period"],
+                "treated": d["treated"],
+                "y": d["y"],
+            }
+        )
+
+    def _fit_absorb(self, d, vcov_type, absorb_cols=("unit",)):
+        return MultiPeriodDiD(vcov_type=vcov_type).fit(
+            self._make_data(d),
+            outcome="y",
+            treatment="treated",
+            time="period",
+            absorb=list(absorb_cols),
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+        )
+
+    def _fit_fixed_effects(self, d, vcov_type, fe_cols=("unit",)):
+        return MultiPeriodDiD(vcov_type=vcov_type).fit(
+            self._make_data(d),
+            outcome="y",
+            treatment="treated",
+            time="period",
+            fixed_effects=list(fe_cols),
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+        )
+
+    def test_absorb_hc2_matches_fixed_effects_dummies_single_absorb(self):
+        """`absorb=["unit"]` + hc2 produces the same per-period SE as
+        `fixed_effects=["unit"]` + hc2 (auto-route invariant)."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        res_a = self._fit_absorb(d, "hc2", absorb_cols=("unit",))
+        res_f = self._fit_fixed_effects(d, "hc2", fe_cols=("unit",))
+        pe_a = res_a.period_effects[target_period]
+        pe_f = res_f.period_effects[target_period]
+        np.testing.assert_allclose(pe_a.effect, pe_f.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_a.se, pe_f.se, atol=1e-12)
+
+    def test_absorb_hc2_result_surface_invariants_multi_absorb(self):
+        """Result-surface contract on the multi-absorb auto-route: the
+        returned `MultiPeriodDiDResults.coefficients` dict must remain
+        complete (one entry per fitted column), uniquely named, and aligned
+        in cardinality with `vcov`.
+
+        Regression for the duplicate-name collision the auto-route would
+        otherwise expose: MPD already includes period dummies in its
+        event-study design, so adding the `time` column as a fixed-effect
+        dummy via `pd.get_dummies(prefix="period", drop_first=True)` would
+        produce a second `period_<X>` block. Under `var_names`-keyed
+        `coef_dict` construction, the duplicates silently collapse and the
+        original event-study period coefficients are overwritten by the
+        rank-deficient NaN drops on the redundant FE block. The fix at
+        `estimators.py:fit()` (skip `fe == time` in the fixed_effects
+        loop) eliminates the duplicate columns entirely. Test pins both
+        the auto-route and the explicit `fixed_effects=` paths.
+        """
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        from collections import Counter
+
+        for kwarg in ("absorb", "fixed_effects"):
+            res = MultiPeriodDiD(vcov_type="hc2").fit(
+                self._make_data(d),
+                outcome="y",
+                treatment="treated",
+                time="period",
+                **{kwarg: ["unit", "period"]},
+                reference_period=int(d["reference_period"]),
+                unit="unit",
+            )
+            assert res.vcov is not None
+            assert len(res.coefficients) == res.vcov.shape[0], (
+                f"[{kwarg}=] coefficients dict length ({len(res.coefficients)}) "
+                f"must match vcov rank ({res.vcov.shape[0]}); duplicate var_names "
+                "collapsed the dict and broke coefficients-vs-vcov alignment."
+            )
+            assert res.vcov.shape[0] == res.vcov.shape[1]
+            dups = {k: v for k, v in Counter(res.coefficients.keys()).items() if v > 1}
+            assert not dups, f"[{kwarg}=] duplicate names in coefficients: {dups}"
+            # Sanity: the event-study period coefficients should be finite
+            # (they are MPD's own non-reference period dummies, NOT the
+            # redundant FE-block that was skipped).
+            pe_name = f"period_{target_period}"
+            assert pe_name in res.coefficients
+            assert np.isfinite(res.coefficients[pe_name]), (
+                f"[{kwarg}=] event-study {pe_name!r} should remain finite "
+                "after the time-FE-skip fix (the duplicate FE-block that "
+                "would have NaN'd it is no longer emitted)."
+            )
+
+    def test_absorb_hc2_matches_fixed_effects_dummies_multi_absorb(self):
+        """`absorb=["unit","time"]` invariant: with both unit and time
+        FE auto-routed, the period dummies collide with time-FE dummies;
+        `solve_ols` handles rank deficiency, slope SE on the
+        target per-period interaction stays well-defined and matches the
+        explicit `fixed_effects=` path."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        # Use "period" as the time/FE column name to match the data column.
+        res_a = MultiPeriodDiD(vcov_type="hc2").fit(
+            self._make_data(d),
+            outcome="y",
+            treatment="treated",
+            time="period",
+            absorb=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+        )
+        res_f = MultiPeriodDiD(vcov_type="hc2").fit(
+            self._make_data(d),
+            outcome="y",
+            treatment="treated",
+            time="period",
+            fixed_effects=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+        )
+        pe_a = res_a.period_effects[target_period]
+        pe_f = res_f.period_effects[target_period]
+        assert np.isfinite(pe_a.se)
+        np.testing.assert_allclose(pe_a.effect, pe_f.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_a.se, pe_f.se, atol=1e-12)
+
+    def test_absorb_hc2_bm_matches_fixed_effects_dummies(self):
+        """`absorb=` + hc2_bm equals `fixed_effects=` + hc2_bm bit-for-bit
+        on both per-period SE and inference (DOF transfers identically)."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        res_a = self._fit_absorb(d, "hc2_bm", absorb_cols=("unit",))
+        res_f = self._fit_fixed_effects(d, "hc2_bm", fe_cols=("unit",))
+        pe_a = res_a.period_effects[target_period]
+        pe_f = res_f.period_effects[target_period]
+        np.testing.assert_allclose(pe_a.effect, pe_f.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_a.se, pe_f.se, atol=1e-12)
+        np.testing.assert_allclose(pe_a.p_value, pe_f.p_value, atol=1e-12)
+
+    def test_absorb_hc2_matches_sandwich_vcovhc(self):
+        """`absorb=` + hc2 matches `lm() + sandwich::vcovHC(type="HC2")`
+        at 1e-10 on the target per-period interaction."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        target_coef = f"treated_period_{target_period}"
+        coef_names = d["coef_names"]
+        idx = coef_names.index(target_coef)
+        expected_vcov = np.asarray(d["vcov_hc2"]).reshape(d["vcov_hc2_shape"])
+        expected_se = float(np.sqrt(expected_vcov[idx, idx]))
+        expected_coef = float(d["coef"][idx])
+
+        res = self._fit_absorb(d, "hc2", absorb_cols=("unit",))
+        pe = res.period_effects[target_period]
+        np.testing.assert_allclose(pe.effect, expected_coef, atol=1e-10)
+        np.testing.assert_allclose(pe.se, expected_se, atol=1e-10)
+
+    def test_absorb_hc2_bm_matches_clubsandwich_singleton_cluster(self):
+        """`absorb=` + hc2_bm matches `clubSandwich::vcovCR(cluster=1:n, type="CR2")`
+        at 1e-10 on the target per-period interaction (singleton-cluster
+        CR2 = one-way HC2-BM by PT2018 §3.3)."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        target_coef = f"treated_period_{target_period}"
+        coef_names = d["coef_names"]
+        idx = coef_names.index(target_coef)
+        expected_vcov = np.asarray(d["vcov_hc2_bm"]).reshape(d["vcov_hc2_bm_shape"])
+        expected_se = float(np.sqrt(expected_vcov[idx, idx]))
+
+        res = self._fit_absorb(d, "hc2_bm", absorb_cols=("unit",))
+        pe = res.period_effects[target_period]
+        np.testing.assert_allclose(pe.se, expected_se, atol=1e-10)
+
+    def test_absorb_plus_fixed_effects_still_rejected_under_hc2_bm(self):
+        """Mutual-exclusion of `absorb=` and `fixed_effects=` is preserved
+        on MPD across all vcov_types (the auto-route does NOT silently merge)."""
+        d = self._load_golden()
+        for vcov in ("hc1", "hc2", "hc2_bm"):
+            with pytest.raises(ValueError, match="Cannot use both absorb and fixed_effects"):
+                MultiPeriodDiD(vcov_type=vcov).fit(
+                    self._make_data(d),
+                    outcome="y",
+                    treatment="treated",
+                    time="period",
+                    absorb=["unit"],
+                    fixed_effects=["period"],
+                    reference_period=int(d["reference_period"]),
+                    unit="unit",
+                )
+
+    def test_absorb_hc2_bm_df_sensitive_inference(self):
+        """HC2 vs HC2-BM produce the same SE on the target per-period
+        interaction but different `p_value` / `conf_int` because the BM
+        Satterthwaite DOF differs from n-k. Guards against an unwired DOF
+        path (R1 review on PR #458 caught the analogous gap on DiD)."""
+        d = self._load_golden()
+        target_period = int(d["target_period"])
+        res_hc2 = self._fit_absorb(d, "hc2", absorb_cols=("unit",))
+        res_hc2_bm = self._fit_absorb(d, "hc2_bm", absorb_cols=("unit",))
+        pe_hc2 = res_hc2.period_effects[target_period]
+        pe_hc2_bm = res_hc2_bm.period_effects[target_period]
+        np.testing.assert_allclose(pe_hc2.effect, pe_hc2_bm.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_hc2.se, pe_hc2_bm.se, atol=1e-12)
+        assert pe_hc2.p_value != pe_hc2_bm.p_value, (
+            "HC2 and HC2-BM should have different p_values "
+            "because the BM Satterthwaite DOF differs from n-k."
+        )
+        width_hc2 = float(pe_hc2.conf_int[1] - pe_hc2.conf_int[0])
+        width_hc2_bm = float(pe_hc2_bm.conf_int[1] - pe_hc2_bm.conf_int[0])
+        assert width_hc2_bm > width_hc2, (
+            f"HC2-BM CI width ({width_hc2_bm:.6f}) should exceed "
+            f"HC2 CI width ({width_hc2:.6f}) — BM Satterthwaite DOF is "
+            "smaller than n-k, so the critical value is larger."
+        )
+
+    def test_absorb_hc2_bm_avg_att_df_sensitive_inference(self):
+        """The post-period-average ATT (`avg_att`, the MPD-specific
+        contrast that does NOT have a DiD analogue) must also reflect
+        the Satterthwaite DOF under HC2-BM: HC2 and HC2-BM share
+        `avg_se` but differ in `avg_p_value` and `avg_conf_int`. This is
+        the MPD-specific inference pin that the DiD test class cannot
+        cover."""
+        d = self._load_golden()
+        res_hc2 = self._fit_absorb(d, "hc2", absorb_cols=("unit",))
+        res_hc2_bm = self._fit_absorb(d, "hc2_bm", absorb_cols=("unit",))
+        np.testing.assert_allclose(res_hc2.avg_att, res_hc2_bm.avg_att, atol=1e-12)
+        np.testing.assert_allclose(res_hc2.avg_se, res_hc2_bm.avg_se, atol=1e-12)
+        assert res_hc2.avg_p_value != res_hc2_bm.avg_p_value, (
+            "HC2 and HC2-BM should produce different `avg_p_value` because "
+            "the BM Satterthwaite DOF on the post-period-average contrast "
+            "differs from n-k. Same p_value indicates the DOF was not "
+            "propagated to the avg_att inference path."
+        )
+        width_hc2 = float(res_hc2.avg_conf_int[1] - res_hc2.avg_conf_int[0])
+        width_hc2_bm = float(res_hc2_bm.avg_conf_int[1] - res_hc2_bm.avg_conf_int[0])
+        assert width_hc2_bm > width_hc2, (
+            f"HC2-BM avg_att CI width ({width_hc2_bm:.6f}) should exceed "
+            f"HC2 avg_att CI width ({width_hc2:.6f}) — BM Satterthwaite "
+            "DOF is smaller than n-k, so the critical value is larger."
+        )
+
+    def test_absorb_hc2_bm_survey_multi_absorb_auto_routes(self):
+        """Survey-weighted multi-absorb + HC2-BM should auto-route, not reject.
+
+        Mirrors the DiD-class test of the same name: the legacy guard at
+        `estimators.py:1505-1512` rejects `survey_design + len(absorb) > 1`
+        because single-pass demeaning is not the correct weighted FWL
+        projection for multiple absorbed dimensions. But when the auto-route
+        fires (hc2/hc2_bm), absorb is swapped for fixed_effects= BEFORE the
+        survey guard sees it, so the demeaning rationale doesn't apply. The
+        auto-route placement is precisely tuned for this case; this test
+        pins it on the MPD path."""
+        from diff_diff import SurveyDesign
+
+        d = self._load_golden()
+        rng = np.random.default_rng(20260420)
+        n = len(d["y"])
+        data = pd.DataFrame(
+            {
+                "unit": d["unit"],
+                "period": d["period"],
+                "treated": d["treated"],
+                "y": d["y"],
+                "weight": rng.uniform(0.5, 2.0, size=n),
+            }
+        )
+        sd = SurveyDesign(weights="weight", weight_type="aweight")
+        # Multi-absorb (unit + period) + survey + hc2_bm: auto-route fires
+        # and the multi-absorb-survey guard is bypassed cleanly.
+        res = MultiPeriodDiD(vcov_type="hc2_bm").fit(
+            data,
+            outcome="y",
+            treatment="treated",
+            time="period",
+            absorb=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+            survey_design=sd,
+        )
+        # Parity invariant: the explicit fixed_effects= path on the same
+        # data must produce the same per-period SE.
+        res_fe = MultiPeriodDiD(vcov_type="hc2_bm").fit(
+            data,
+            outcome="y",
+            treatment="treated",
+            time="period",
+            fixed_effects=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+            survey_design=sd,
+        )
+        target_period = int(d["target_period"])
+        pe_a = res.period_effects[target_period]
+        pe_f = res_fe.period_effects[target_period]
+        assert np.isfinite(pe_a.effect)
+        assert np.isfinite(pe_a.se)
+        np.testing.assert_allclose(pe_a.effect, pe_f.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_a.se, pe_f.se, atol=1e-12)
+        np.testing.assert_allclose(res.avg_att, res_fe.avg_att, atol=1e-12)
+        np.testing.assert_allclose(res.avg_se, res_fe.avg_se, atol=1e-12)
+
+    def test_absorb_hc2_bm_replicate_weights_auto_routes(self):
+        """Replicate-weight survey design + absorb + HC2-BM auto-routes
+        through `compute_replicate_vcov` on the full-dummy design.
+
+        The CHANGELOG/REGISTRY claim that, under the auto-route, the
+        survey-replicate absorb-refit branch at `estimators.py:1693` is
+        short-circuited (no per-replicate refit needed because the
+        full-dummy design does not depend on replicate weights — the
+        standard `compute_replicate_vcov` path applies directly). This
+        test pins the parity invariant on a JK1 fixture: `absorb=`
+        + replicate weights must produce the same `period_effects`
+        and `avg_att` SEs as the explicit `fixed_effects=` form."""
+        from diff_diff import SurveyDesign
+
+        d = self._load_golden()
+        rng = np.random.default_rng(20260420)
+        n = len(d["y"])
+        data = pd.DataFrame(
+            {
+                "unit": d["unit"],
+                "period": d["period"],
+                "treated": d["treated"],
+                "y": d["y"],
+                "weight": rng.uniform(0.5, 2.0, size=n),
+            }
+        )
+        # 10 JK1 jackknife replicate-weight columns; weights drawn from
+        # {0.5, 1.5} match the BRR pattern of the existing replicate
+        # tests in this file.
+        rep_cols = [f"rep{r}" for r in range(10)]
+        for col in rep_cols:
+            data[col] = rng.choice([0.5, 1.5], size=n)
+        sd = SurveyDesign(
+            weights="weight",
+            replicate_weights=rep_cols,
+            replicate_method="JK1",
+            replicate_scale=1.0,
+        )
+        res_absorb = MultiPeriodDiD(vcov_type="hc2_bm").fit(
+            data,
+            outcome="y",
+            treatment="treated",
+            time="period",
+            absorb=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+            survey_design=sd,
+        )
+        res_fe = MultiPeriodDiD(vcov_type="hc2_bm").fit(
+            data,
+            outcome="y",
+            treatment="treated",
+            time="period",
+            fixed_effects=["unit", "period"],
+            reference_period=int(d["reference_period"]),
+            unit="unit",
+            survey_design=sd,
+        )
+        target_period = int(d["target_period"])
+        pe_a = res_absorb.period_effects[target_period]
+        pe_f = res_fe.period_effects[target_period]
+        assert np.isfinite(pe_a.effect)
+        assert np.isfinite(pe_a.se)
+        # The auto-route short-circuits the absorb-refit branch and routes
+        # both calls through the standard replicate-vcov path; SE parity
+        # is therefore exact (bit-identical, not just to within 1e-10).
+        np.testing.assert_allclose(pe_a.effect, pe_f.effect, atol=1e-12)
+        np.testing.assert_allclose(pe_a.se, pe_f.se, atol=1e-12)
+        np.testing.assert_allclose(res_absorb.avg_att, res_fe.avg_att, atol=1e-12)
+        np.testing.assert_allclose(res_absorb.avg_se, res_fe.avg_se, atol=1e-12)