ENH: Add Hansen-Lee misspecification-robust J-test to IVGMM

[hass-nation]

Closes #430

What this adds

IVGMMResults.robust_j_stat — a misspecification-robust J-test from Hansen & Lee (2021, Econometrica 89(3), 1419-1447).

The standard J-statistic uses the uncentered moment covariance as its weight matrix. Under misspecification (E[g(z,θ)] != 0), this causes the test to saturate — it stays bounded by n rather than diverging. The robust statistic replaces the uncentered covariance with the centered covariance S_c = (1/n) sum (g_i - g_bar)(g_i - g_bar)' giving J* = n g_bar' S_c^{-1} g_bar ~ chi2(q) under correct specification and diverging at rate n under misspecification.

API

res = IVGMM(y, exog, endog, instr).fit(cov_type="robust")
res.j_stat         # existing: standard J-test
res.robust_j_stat  # new: Hansen-Lee robust J-test
print(res.summary) # shows both side-by-side in the header table

Works for all cov_type values: robust, unadjusted, kernel, clustered.

Implementation

• _IVGMMBase._hansen_lee_j_statistic: computes J* using the centered weight matrix estimator matching cov_type
• _gmm_post_estimation in both _IVGMMBase and IVGMM: threads cov_type/cov_config through and adds robust_j_stat to the results dict
• IVGMMResults.robust_j_stat property with full docstring and math
• IVGMMResults._top_right: summary header now shows J-stat, HL J-stat, p-values, distributions, and iterations
• Zero new dependencies

Tests: 14 new, 6515 existing all pass

• test_robust_j_stat_formula_matches_manual_calculation: J* = n g_bar' S_c^{-1} g_bar exactly
• test_robust_j_algebraic_identity_iterated_gmm: J* = J*n/(n-J) for iterated GMM
• test_robust_j_stat_nonnegative_all_cov_types: valid for robust/homo/kernel/clustered
• test_ivgmmcue_has_robust_j_stat: IVGMMCUE also exposes robust_j_stat
• test_summary_contains_hl_j_statistic: both stats shown in summary

References

Hansen, B. E. & Lee, S. (2021). Inference for iterated GMM under misspecification. Econometrica, 89(3), 1419-1447.

[codecov]

Codecov Report

❌ Patch coverage is 98.49624% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 99.53%. Comparing base (b535f55) to head (299e3d1).

Files with missing lines	Patch %	Lines
linearmodels/iv/model.py	90.90%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #701      +/-   ##
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- Coverage   99.54%   99.53%   -0.01%     
==========================================
  Files         101      102       +1     
  Lines       17426    17557     +131     
  Branches     1430     1437       +7     
==========================================
+ Hits        17347    17476     +129     
- Misses         29       30       +1     
- Partials       50       51       +1     

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[bashtage]

Please fix the ruff linting error.

[bashtage]

Need Ruff fix, and probably to be blackened. Should probably run isort on the changed files as well.

Oh no! 💥 💔 💥
5 files would be reformatted, 124 files would be left unchanged.
Skipped 1 files
RUF002 Docstring contains ambiguous `–` (EN DASH). Did you mean `-` (HYPHEN-MINUS)?
    --> linearmodels/iv/model.py:1098:54
     |
1096 |         ----------
1097 |         Hansen, B. E. & Lee, S. (2021). Inference for iterated GMM under
1098 |         misspecification. *Econometrica*, 89(3), 1419–1447.
     |                                                      ^
1099 |         """
1100 |         y, x, z = self._wy, self._wx, self._wz
     |

RUF002 Docstring contains ambiguous `–` (EN DASH). Did you mean `-` (HYPHEN-MINUS)?
    --> linearmodels/iv/results.py:1503:54
     |
1501 |         ----------
1502 |         Hansen, B. E. & Lee, S. (2021). Inference for iterated GMM under
1503 |         misspecification. *Econometrica*, 89(3), 1419–1447.
     |                                                      ^
1504 |         """
1505 |         return self._robust_j_stat
     |

F841 Local variable `expected_df` is assigned to but never used
  --> linearmodels/tests/iv/test_hansen_lee_j_stat.py:78:5
   |
76 |     ninstr = data.instr.shape[1] + data.exog.shape[1]
77 |     nendog = data.endog.shape[1]
78 |     expected_df = ninstr - nendog - data.exog.shape[1]
   |     ^^^^^^^^^^^
79 |     # df = total instruments - total params = (nexog+ninstr) - (nexog+nendog)
80 |     #    = ninstr - nendog
   |
help: Remove assignment to unused variable `expected_df`

Found 3 errors.
No fixes available (1 hidden fix can be enabled with the `--unsafe-fixes` option).
linearmodels/tests/iv/test_hansen_lee_j_stat.py:78:5: F841 local variable 'expected_df' is assigned to but never used