NEWS

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mvardlurt 1.1.1

• Documentation only; no computation changed and all results are identical to 1.1.0. The help page of mvardlurt() (Details) and the README now state that the lag orders p and q of the bootstrap are those of the test regression (fixed through fixlag or selected by AIC or BIC on the observed data) and are held fixed in every bootstrap replication; the lag selection is not repeated on the bootstrap samples. This follows Section 4.2 of Sam, McNown, Goh and Goh (2025), where the restricted regression (24) is estimated with the lag length of the data generating process and equation (1) is re-estimated on y* and x. The help page also reports a Monte Carlo experiment by the package author (n = 100, y and x independent random walks, 200 replications, 199 bootstrap replications, 5 percent level): rejection frequencies 0.055 to 0.070 with fixed lag orders and 0.080 to 0.100 with AIC selection (Monte Carlo standard error about 0.02).

mvardlurt 1.1.0 (2026-10-01)

This release corrects the bootstrap and the interpretation of the test so that they follow Sam, McNown, Goh and Goh (2025), Studies in Economics and Econometrics 49(1), 17-33, doi:10.1080/03796205.2024.2439101. The version on CRAN is 1.0.2.

Breaking changes

• level is now the significance level of the two tests (default 0.05, the level used in the paper's simulations); in 1.0.2 it was a confidence level (default 0.95) that was stored but never used, the decision always being made at 10 percent. Values of 0.5 or more are rejected with a message pointing to 1 - level.
• The lag convention follows equation (1) of the paper: an ARDL(p, q) model has p - 1 lagged differences of y and q - 1 lagged differences of x (p, q at least 1). fixlag = c(p, q) in 1.0.2 corresponds to fixlag = c(p + 1, q + 1) in 1.1.0 (plus the contemporaneous difference of x, see below). The paper itself is inconsistent: Section 4.1 and Table 4 count lagged differences directly (the 1.0.2 convention), so the paper's ARDL(0, 2) is fixlag = c(1, 3) here. maxlag must be between 1 and 12.
• reps was renamed to nboot (default 999, minimum 99); reps still works with a deprecation warning. pi_coef, pi_se, delta_coef and delta_se were renamed to b1, b1_se, beta2 and beta2_se; fstat_p was removed (see below).

Corrections

• Bootstrap (Section 4.2 of the paper). Version 1.0.2 simulated y* and x* as independent Gaussian random walks without any estimated coefficient and without resampling residuals, so the critical values did not depend on the data (Monte Carlo size of the t test with drift was 0.010 at the nominal 5 percent level). The t and F statistics are now bootstrapped separately with the respective null imposed: the restricted regression (without y[t-1] for the t test, without x[t-1] for the F test) is estimated, its recentred residuals are resampled with replacement, y* is generated recursively from the restricted estimated equation with the observed x held fixed, and the unrestricted regression is re-estimated on (y*, x). The contemporaneous difference of x, which equation (24) of the paper omits although equation (1) has it, is included in the restricted regressions and in the bootstrap, following equation (1). Bootstrap critical values at 10, 5, 2.5 and 1 percent and bootstrap p-values (t_pval, f_pval) are reported; the bootstrap distributions are returned in t_boot and f_boot.
• Four cases (Section 3.2 of the paper). The labels were wrong in 1.0.2. They are now: Case I, neither test rejects, y is I(1) with no cointegration; Case II, only the t test rejects, y is I(0); Case III, only the F test rejects, y is I(2); Case IV, both reject, y is I(1) through cointegration with x. The order of integration is stated in the output (decision$integration). The wording "degenerate case", "spurious" and "H0: delta = 0, no cointegration" was removed; the F test is for H0: beta2 = 0 (equation (12)).
• Test regression (equation (1) of the paper). The contemporaneous difference of x is now included in the regression, in both restricted regressions and in the bootstrap (equation (1); see Breaking changes for the lag convention).
• x may be a matrix with several covariates; the F statistic is then the joint Wald F test on all lagged levels. beta2, beta2_se and lr_mult have one element per covariate, and coef() returns b1, beta2.<name> and lr_mult.<name>.
• The decision uses level (see Breaking changes). The significance codes for 10, 5, 2.5 and 1 percent are still reported.
• seed now defaults to NULL. When a seed is supplied the caller's RNG state is saved and restored on exit, so a call no longer resets the global random number stream (1.0.2 called set.seed(12345) unconditionally).
• fstat_p, a p-value from the standard F distribution that is invalid here (Section 3.3 of the paper), was removed in favour of the bootstrap p-value.
• Lag selection compares all candidate models on a common sample (t = maxlag + 1, ..., n); 1.0.2 compared AIC or BIC values computed on different samples. The IC table is indexed by p = 1, ..., maxlag and q = 1, ..., maxlag.
• The citation of the paper was corrected to volume 49(1), pages 17-33, 2025 (it read 2024, 1-17).
• The data frame of the test regression is returned as data; the model component is for inspection, and can be refitted with lm(formula(object$model), data = object$data).

mvardlurt 1.0.0

• Initial CRAN release.
• Implements the multivariate ARDL unit root test (Sam, McNown, Goh and Goh, 2025) [citation corrected in 1.1.0].
• Features:
  • Automatic lag selection via AIC/BIC
  • Bootstrap critical values for correct size
  • Three deterministic cases (none, intercept, intercept + trend)
  • Four-case decision framework for inference
  • Comprehensive print and summary methods
  • Diagnostic plots