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xtpcaus(), PFTY test: zbar and
zbar_pv are NA unless kmax = 0
and dmax = 0 (plain VAR(p) specification). For that
specification they are the Dumitrescu and Hurlin (2012) Z-bar of eq. (9)
computed from the individual Wald statistics with the unbiased residual
variance, and zbar_pv is one-sided (upper tail). The 1.1.6
Z-bar (asymptotic moments applied to the Fourier Toda-Yamamoto Wald
statistics, two-sided p-value) is no longer computed.xtpcaus(), PFTY test: lag orders whose unrestricted
regression leaves fewer than 5 residual degrees of freedom are not
considered; a warning reports the bound, and the function stops when no
lag order is admissible. Selected lags and Wald statistics change on
short panels where version 1.1.6 selected such a lag order.xtpcaus(), PFTY test: bootstrap p-values change for the
same seed. The bootstrap now resamples the residuals of the restricted
model (tested lags of x removed), draws the same time indices for all
units (cross-sectional block of size N, Dumitrescu and Hurlin 2012,
Section 6.2), generates each unit’s bootstrap series recursively from
the restricted model and repeats the lag and frequency selection on it;
version 1.1.6 resampled the unrestricted residuals independently per
unit on a fixed design with the selected lag and frequency held
fixed.xtpcaus(), PFTY test: a unit whose regressor matrix is
singular has ind_wald, ind_pval_b set to
NA with a warning and is excluded from the Fisher, W-bar
and Z statistics (fisher_df = 2 * N_used); version 1.1.6
set its Wald statistic to 0 with p-value 1.xtpcaus(), PFTY test: kmax = 0 (no Fourier
terms) is now a specification of its own. In 1.1.6 kmax = 0
was accepted without error, but the frequency loop never ran, so every
unit was fitted with the lag order p = 1 and the frequency f = 1 (a
Fourier regression with no lag selection). Results with
kmax = 0 change accordingly; for
grunfeld_panel() with pmax = 2,
dmax = 0 the selected lag orders change from 1, 1, 1, 1, 1
to 1, 2, 2, 2, 2, the selected frequencies from 1 to 0, and
W-bar from 1.3603 to 4.2288.xtpcaus(), PFTY test: new elements ztilde,
ztilde_pv (Dumitrescu and Hurlin 2012, eq. 26 with the
moments of eq. 21 and 22, one-sided p-value),
ztilde_boot_pv (one-sided cross-sectional block bootstrap
p-value), wbar_dh, ind_wald_dh,
ind_df, N_used. The Z-tilde elements are
NA unless kmax = 0 and
dmax = 0.xtpcaus(): dmax = 0 omits the
Toda-Yamamoto augmentation.xtpcaus(): missing values in y,
x, panel_id or time_id stop with
the count and row positions.xtpcaus(): every unit must be observed at the same set
of time values exactly once; duplicated or misaligned rows stop with an
error (version 1.1.6 compared only the row count with N times T).xtpcaus(): seed is applied in a local RNG
scope; the global .Random.seed is restored on exit.print.xtpcaus(): PFTY output shows the degrees of
freedom per unit, the Dumitrescu and Hurlin statistics for the plain
specification, and a note instead of Z-bar for the other specifications;
the arrow symbol in the PQC labels replaced by “to”.xtpcaus(); Details section describing the specification,
the degrees-of-freedom bound, the Z statistics and the bootstrap; “and”
instead of “&” in the printed test names of caustests()
and xtpcmg().lm() for the plain and the Fourier specifications, the
degrees-of-freedom error, the missing-value error, the misaligned-panel
error, the singular-unit warning and the RNG scope.inst/validation/ledger_xtpcaus.md.xtpcaus(), PFTY test: the Z-bar statistic is documented
as asymptotic in T (sequential limit, T before N). On short panels (for
example T = 17) it rejects at p-values near zero even when the bootstrap
Fisher statistic does not; the printed output now carries a note
recommending the bootstrap Fisher p-value in that case. No change to the
computation.quantreg::summary.rq(se = "nid") warns “k non-positive fis”
when the sparsity estimate is non-positive at some observations. The
package called it once for the observed sample and once per bootstrap
replication at every quantile, so the same warning was repeated up to
(nboot + 1) times per quantile and direction (for example 965 times for
test 7 on caustests_data with 5 quantiles and
nboot = 99), which users mistook for an error. These
warnings are now counted inside the loops and reported once per call,
with the directions, quantiles and number of fits affected. Other
warnings are not suppressed.quantreg_warnings in the returned object
(tests 6-7): a data frame with the number of affected quantile
regression fits per direction and quantile. print() adds
one line when any occurred.xtpcmg(): the one-sided long-run covariance used in the
fully modified bias correction was transposed (it estimated the sum of
E(u_t v_{t+j}) instead of E(v_t u_{t+j})), and the quadratic spectral
and Daniell kernels did not use the one-sided weights of the authors’
code; group-mean and pooled FM-OLS estimates were therefore biased. The
long-run covariance now follows the authors’ lr_varmod.m.xtpcmg(), pooled model: the covariance matrix is now
the asymptotic covariance of de Jong and Wagner (2022) for one-way and
two-way effects (as in the authors’ PanelEKC code), with a
heteroskedasticity-robust sandwich for the controls; the previous
version used sigma^2 (X’X)^-1 from the FM residuals, and a unit matrix
when X’X was singular.xtpcmg(), cross-section robust covariance
(corr_rob = TRUE): uses the conditional long-run covariance
between units instead of the covariance of u alone.xtpcmg() estimates and standard errors now
reproduce the Stata module xtpcmg 1.0.2; tests added.dmax
lags, so the selected p was that of the augmented model. The lag order p
is now selected on the VAR in levels with p lags, and the test VAR then
has p + dmax lags, as in Toda and Yamamoto (1995).print(), summary(),
plot()caustests_dataThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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