ardlverse 2.0.0 - Major bug fixes (Levchenko, KSE)

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).