ardlverse 2.0.0

Major bug fixes in panel_ardl()

Thanks to Yeleazar (Lazar) Levchenko (Kyiv School of Economics), who audited panel_ardl() against Stata’s xtpmg (Blackburne & Frank 2007) and contributed corrections that bring the implementation into strict alignment with the original Pesaran, Shin & Smith (1999) framework.

Seven issues were identified and fixed:

1. Missing intercepts in short-run regressions. The original code used lm.fit() for internal regressions; unlike lm(), lm.fit() does not append an intercept. All short-run regressions across PMG, MG, and DFE were forced through the origin. A column of 1s is now bound to the design matrices, and DFE reconstructs the grand-mean intercept to match standard fixed-effects output.

2. Misaligned error-correction term in .prepare_ardl_data. The long-run matrix (X_levels) was constructed from rows 1 to (n-1), pairing the lagged dependent variable y(t-1) with lagged X(t-1). The standard ARDL error-correction term requires y(t-1) paired with contemporaneous X(t). Indexing corrected to rows 2 through n.

3. Statistically invalid Hausman test. The previous test isolated only diagonal variances and used abs() to force-ignore negative variance differences, bypassing the covariance structure and invalidating the chi-squared statistic. The test is now built on the proper matrix quadratic form, with a new sigmamore = TRUE argument (matching Stata) that rescales the inefficient variance matrix when the difference matrix is non-positive-definite.

4. Incorrect PMG standard errors. The previous code computed PMG SEs from the cross-sectional standard deviation of group-specific long-run estimates, contradicting PMG theory (long-run coefficients are constrained to be homogeneous). Replaced with the exact PSS

   1999. Information Matrix formulation using the G-matrix blocks.

5. Simplified delta method for DFE standard errors. The previous code assumed zero covariance between short-run coefficients and the error-correction parameter. The full multivariate delta method with the proper Jacobian is now used.

6. Incorrect MG standard errors. Previously computed naively as SD / sqrt(N). Replaced with the exact cross-sectional variance-covariance formula used by Blackburne & Frank (2007).

7. Sub-optimal PMG initialization. The previous code ran the full MG estimator to generate PMG starting values. PMG is now initialized from a simple pooled OLS of the lagged dependent variable on the levels of X — faster, avoids convergence risk if MG fails, and matches Stata’s exact initialization.

Breaking changes

• hausman_test() signature changed to follow Stata’s convention:

  • Old: hausman_test(pmg_model, mg_model, data)
  • New: hausman_test(inefficient, efficient, sigmamore = TRUE)

  Pass the inefficient (always consistent) estimator first (typically MG), then the efficient one (typically PMG). The previous third argument data is removed; required information is read from the model objects.

• Internal helpers .estimate_mg_internal() and .compute_pmg_se() have been consolidated into .estimate_mg() and .estimate_pmg() respectively. Code that imported these internal functions (which is not supported usage) will need to be updated.

Other changes

• New optional arguments to panel_ardl(): start_time (restrict estimation to observations at or after a given time) and cluster (cluster-robust SEs where applicable).
• Convergence tolerance tightened from 1e-5 to 1e-6 by default.
• Reference replication script (replicate_jasa.R, validating against Blackburne & Frank 2007) added to the test suite.

ardlverse 1.1.3

• Initial CRAN release of comprehensive ARDL framework (Panel, Bootstrap, Fourier, Quantile, Augmented, NARDL, Rolling/Recursive).