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model = "dfe": the standard errors of rho and beta and
the cross-quantile blocks of rho_V and beta_V
come from one joint covariance matrix, the Koenker and Bassett (1982,
Theorem 3.1) form with the scalar sparsity of
summary.rq(se = "iid") (new argument dfe_cov,
default "iid"). In 1.0.4 the diagonal blocks were the
Powell kernel sandwich (se = "ker") and the off-diagonal
blocks the Hendricks-Koenker form (se = "nid"). The
estimator is stored in dfe_cov and printed by
print() and summary().
dfe_cov = "nid" and "ker" use the
corresponding summary.rq estimator for every block; the man
page documents the Monte Carlo size of each choice.wald_test(): the Koenker-Bassett tests in
kb_test restrict only the economic slopes (rho, theta,
short run) and are computed from the joint matrix of the fit; in 1.0.4
they were quantreg::anova(..., se = "nid") and also
restricted the N - 1 panel-intercept contrasts. New argument
all = TRUE restricts every non-intercept coefficient; with
dfe_cov = "nid" this reproduces the 1.0.4 values when the
quantile regression solutions are unique (when they are not, the
ordering of the panel contrasts can change the Hendricks-Koenker
densities and so the values). kb_test now holds
restricted, se and all; the
printed labels state which coefficients are restricted.time - j within the panel), not by row position.
Duplicated (id, time) rows are an error. A gap
in a panel’s time index is an error unless the new argument
allow_gaps = TRUE is given; rows whose lagged period is
missing are then dropped. The time variable must be
numeric.lagsel: the information criteria count the coefficients
of all panel regressions (N times the per-panel count); in 1.0.4 only
one panel’s coefficients were counted. The man page states that the
selection is by least squares.model = "pmg": hausman is a data frame
with one row per quantile (statistic, df, p-value, number of
non-positive eigenvalues, method). The test is NA with a
warning when V(MG) - V(PMG) is not positive definite (new argument
hausman = "strict", the default);
hausman = "ginv" uses a Moore-Penrose inverse with a
warning. In 1.0.4 one statistic over all quantiles was computed with the
PMG cross-quantile blocks set to zero and negative eigenvalues dropped
silently.summary() prints the Hausman table; in 1.0.4 the
printing code was never reached.rq_fits (DFE): 1.0.4 stored rq(y ~ XX)
fits with coefficients named (Intercept),
XXL_y, …, and panel-contrast levels in character order.
1.0.5 stores rq(y ~ XX - 1) fits whose coefficients are the
slopes, then (Intercept), then the panel contrasts, named
by the id variable followed by the panel level (for example
countryCountry_B), in panel order. Code that indexes
coef(fit$rq_fits[[1]]) by name or position must be
updated.N < 3) is
issued for model = "pmg" as well as
model = "mg", since both use the same panel code.log(), I() and interactions
are expanded with model.matrix before the lags are built;
the column names of the expanded design are the regressor names.summary.rq(se = "iid") (MG, PMG), when a panel
is dropped from the PMG pooling, when a DFE covariance block cannot be
estimated (the block is NA), and when
summary.rq reports non-positive density estimates.
panel_cov_status is also returned for DFE.model = "mg" with fewer than three panels warns.dfe_coef, dfe_coef_V,
dfe_slopes, n_dropped, time_step;
summary() prints the number of rows dropped for missing
values.xtpqardl and
wald_test.print.wald_test() no longer writes text arrows.quantreg::anova
reproduction now uses dfe_cov = "nid" and
all = TRUE; the PMG short-panel test accepts the new switch
warning. New tests pin the iid joint covariance, the “nid” and “ker”
blocks, the restriction count of the joint test, time-aligned lags on a
panel with a gap, the collinearity and formula handling, the
NA covariance path, the Hausman rule and the lag-selection
penalty to hand computations.wald_test(fit, joint = FALSE) failed inside
print() with “$ operator is invalid for atomic vectors”
whenever the covariance matrix had NA blocks (every DFE and
PMG fit in 1.0.3), because the helper returned one flat list instead of
one list per variable. Fixed, and print() now guards
against malformed elements.model = "dfe" the cross-quantile covariance
blocks of beta_V and rho_V are estimated with
the Koenker and Bassett (1982) formula (all quantiles share the pooled
design), so the cross-quantile Wald tests of wald_test()
are now available for DFE. wald_test() additionally returns
kb_test, the quantreg::anova(..., se = "nid")
equality tests (joint and per coefficient) on the pooled
quantile-regression coefficients; these reproduce
anova(rq_tau1, rq_tau2, ...) on the equivalent regression
d_y ~ L_y + L_x + d_x + factor(id). The rq
fits are kept in rq_fits.summary.rq, which fails
when the degrees of freedom per panel are very few (the study data had n
- k = 2); in 1.0.3 every panel was then silently dropped and PMG stopped
with “needs at least two panels with a usable long-run covariance”. The
function now tries the other summary.rq estimators and an
iid formula with a kernel sparsity estimate before giving up, reports
the status and reason for every panel and quantile
(panel_cov_status, also for MG), explains the
degrees-of-freedom problem in the error message, and offers
pmg_weights = "equal" (unweighted pooling).wald_test(): the singularity check
det(RVR') < 1e-15 declared the test “Singular variance
matrix” whenever the dependent variable had a small scale (the
determinant of a matrix of small variances is tiny even when it is well
conditioned); it now uses the reciprocal condition number
(rcond < 1e-12).lr must not contain the dependent
variable of formula, nor any of its regressors, and all
variables must be numeric; the previous behaviour was “subscript out of
bounds” or a silent run with a wrong error correction term.lr were lagged a
second time, so with lr = c("L_y", ...) the error
correction term used y(t-2). They now enter exactly as supplied.model = "pmg" returned the Mean Group
estimator. PMG now pools the long-run coefficients by minimum distance
(inverse delta-method covariance weights) and re-estimates each panel
with the pooled long run imposed; a Hausman test of long-run homogeneity
(MG against PMG) is returned in hausman.model = "dfe" returned fixed placeholder
variances (0.01 on the diagonal). Its standard errors now come from the
Powell kernel sandwich covariance of the pooled quantile regression,
transformed by the delta method.NA, so wald_test() reports them
as unavailable instead of assuming independence.These 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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