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Changelog

All notable changes to this project are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.3.3] — 2026-08-12

Changed

  • jarque_bera_test now computes skewness/kurtosis via StatsBase.skewness/ StatsBase.kurtosis instead of hand-rolled moment formulas (identical result; StatsBase.kurtosis is excess kurtosis, so the - 3.0 term moved into the library call).
  • The three residual-standardization plot recipes (:residuals, :histogram, :qq) now use StatsBase.zscore instead of a repeated (x .- mean(x)) ./ std(x) expression.
  • StatsAPI.nobs(model; count_groups=true) removed — it duplicated the existing ngroups(model) accessor. Use ngroups(model) for the number of groups; nobs(model) now only returns total observations.

[0.3.2] — 2026-07-15

Added

  • Regression tests for three previously-uncovered defensive branches, found via line-coverage inspection rather than code reading alone: build_instruments's abstract-model fallback (must raise an informative error, not a generic MethodError), ar_test's degenerate total_variance <= 0 guard (all-zero residuals), and wald_test's near-singular R*V*R' regularization branch.

Fixed

  • estimate now rejects collinear regressors (rank(X) < n_regressors) with a clear error naming the affected columns, instead of failing deep inside the GMM solver with an opaque LinearAlgebra.SingularException. Found via adversarial testing (e.g. a regressor and an exact rescaling of it), not previously covered by any test.
  • examples/example.jl's diagnostics comment mischaracterized the AR(1)/AR(2) results: AR(1) is expected to be significant after differencing (not a misspecification signal, per the Getting Started guide); only AR(2) significance would indicate misspecification. Reworded for precision after running the example against live data and checking the actual output.

Performance

  • calculate_clustered_weight_matrix and _ab_h_weight_matrix's per-cluster accumulation loops used non-mutating A += ..., reallocating the full n_instruments × n_instruments matrix on every individual/cluster instead of accumulating in place; changed to A .+= ... (identical result, no behavior change). Benchmarked on simulated panels: roughly 20-28% less memory allocated by fit at N=20,000-50,000 (e.g. two-step DifferenceGMM at N=50,000, T=10: ~4.08 GB → ~2.97 GB), confirming the README's large-N performance claim under an actual adversarial/stress-test pass rather than just reading the sparse-matrix code.

[0.3.1] — 2026-07-14

Added

  • lag documented in docs/src/api.md (it was exported but missing from the API reference).
  • Behavioral regression tests: collapse/max_lags are now verified to actually change fit's returned coefficients/vcov (not just the intermediate instrument-matrix column count), and SystemGMM(windmeijer=false) is now verified to skip the correction the same way the existing DifferenceGMM case does.

Changed

  • DifferenceGMM/SystemGMM are now generated from a shared @dynpanel_spec macro to remove duplicated boilerplate between the two model spec structs; fields and behavior are unchanged.

Fixed

  • DifferenceGMM's one-step weighting matrix used plain (Z'Z)^-1 instead of the Arellano-Bond (1991) (Z'HZ)^-1 (H reflecting the MA(1) structure of differenced homoskedastic errors). Coefficients were only mildly affected, but the one-step Sargan/Hansen J-statistic was biased downward by roughly a factor of the number of groups. Validated against the EmplUK dataset from the original paper (Table 4): one-step Sargan chi2(25) now ≈70.9 vs. the paper's 65.8, versus ≈0.5 before the fix.
  • The new DifferenceGMM weighting-matrix code re-densified the sparse instrument matrix once per individual instead of once upfront, and called Matrix() twice on the same slice; fixed to match the existing densify-once pattern already used elsewhere in the solver. The cluster-boundary-walking loop this needed was also duplicated three times across the file; extracted into a shared, memoized _cluster_ranges helper (results threaded via a ranges keyword so callers don't recompute them).
  • calculate_clustered_weight_matrix/calculate_windmeijer_correction now guard that an explicitly passed ranges keyword argument is actually consumed, instead of silently ignoring it.
  • Added a genuine (non-tautological) Windmeijer correction regression test against a hand-computed expected value, replacing a check that only verified internal wiring.
  • _lag_vector's (id, time) => row index map was rebuilt from scratch for every regressor column in ar_test (once per column, all with Any-typed Dict keys causing boxing on every insert/lookup); it is now built once per ar_test call, concretely typed on eltype(id)/eltype(time), and reused across all columns.
  • _solve_gmm densified the instrument matrix Z unconditionally, even on the one-step, non-robust path where the densified copy is never used (calculate_clustered_weight_matrix is only called when steps == 2 or robust); it is now only densified when actually needed.

[0.3.0] — 2026-07-08

Added

  • Difference GMM (Arellano-Bond, 1991) and System GMM (Blundell-Bond, 1998) estimators, plus Anderson-Hsiao (1981) as a baseline IV estimator; one-step and two-step estimation with cluster-robust and Windmeijer (2005) finite-sample corrected standard errors.
  • Forward orthogonal deviations (transform=:fod, Arellano-Bover) for Difference GMM, as an alternative to first differencing (better for unbalanced panels).
  • exog keyword: strictly exogenous regressors are used as their own ("IV-style") GMM instruments (Roodman, 2009). A lag of a strictly exogenous covariate may itself be exogenous; the dependent variable and its lags may not.
  • time_effects keyword: adds T-2 exogenous period dummies to absorb common time shocks.
  • xtabond2-style instrument lag limits (min_lag, max_lag) and automatic dropping of linearly dependent instrument columns (drop_collinear, on by default).
  • Richer formula syntax: whitelisted transforms (log, log10, log2, exp, sqrt, abs) on either side and nested in lags (log(y) ~ lag(log(y)) + log(x)); explicit and ranged lags (lag(x, 2), lag(x, 0:1)).
  • StatsModels @formula support (formula = @formula(y ~ lag(y) + x)) in addition to the string form, plus the lag formula marker.
  • Tables.jl input: fit/get_diff_data accept any Tables-compatible source, not only DataFrame.
  • Diagnostics: Sargan/Hansen J-test, Arellano-Bond AR(1)/AR(2) tests (with cached results, so ar_test(model, order) works after diagnose), Wald test, Jarque-Bera test, pseudo-R², instrument-proliferation check, and diff_hansen_test (a Durbin-Wu-Hausman-style difference-in-Hansen test for a nested instrument subset).
  • StatsAPI RegressionModel interface (coef, vcov, stderror, confint, …), Stata-like result printing, and diagnostic plot recipes.
  • Aqua.jl quality tests; ~99% line coverage.
  • Package infrastructure: GitHub Actions CI (+ CompatHelper, TagBot), Documenter.jl docs, .JuliaFormatter.toml, CHANGELOG.md, CONTRIBUTING.md, and integration tests against a simulated panel with known parameters.

[Unreleased]: https://github.com/MichalS16/DynamicPanelModels.jl/compare/v0.3.3...HEAD [0.3.3]: https://github.com/MichalS16/DynamicPanelModels.jl/compare/v0.3.2...v0.3.3 [0.3.2]: https://github.com/MichalS16/DynamicPanelModels.jl/compare/v0.3.1...v0.3.2 [0.3.1]: https://github.com/MichalS16/DynamicPanelModels.jl/compare/v0.3.0...v0.3.1 [0.3.0]: https://github.com/MichalS16/DynamicPanelModels.jl/releases/tag/v0.3.0