prais 1.2.0
- Added Michael Škvrňák as a contributor.
- Fixed
vcovPC.prais for panels that do not all begin in
the same period. The covariances of the panels were indexed by the order
in which the panels first appear, while the observations of a period
follow the panel, so the covariances were assigned to the wrong panels.
The results now agree with package pcse.
- The covariance matrix of the coefficients is obtained from the QR
decomposition of the transformed model matrix. Inverting its cross
product forms the normal equations, which squares the condition number
and cost accuracy if the regressors are close to collinear.
- The observations are brought back into the order of argument
index after the model frame was built. A
subset that reorders the observations undid that order and
made the transformation use the wrong lags.
- Estimation and
summary.prais are considerably faster
for data with many panels, and the check of argument index
and the alignment of the observations no longer dominate the run time of
larger samples. The positions of the observations were obtained by
appending to a vector once per panel, which copies the whole vector in
every iteration.
- Changed the license to GPL (>= 2). Package
pcse,
which prais depends on, is licensed under GPL (>= 3),
with which GPL-2 alone is not compatible.
- Fixed the handling of missing values in
prais_winsten.
Observations that were dropped by lm are now also removed
from the data that is used to construct the panel and lag positions,
which previously caused errors such as “subscript out of bounds” or
“NA/NaN/Inf in ‘y’”.
prais_winsten now throws an informative error if the
variables specified in argument index contain
NA values.- Fixed argument
newdata of predict.prais,
which was positioned after ... and was therefore ignored
when it was not passed by name. predict(object, newdata)
silently returned the fitted values instead of the predictions.
predict.prais now builds the model matrix of
newdata from the model terms. Transformed variables,
factors and interactions are handled correctly, and the variables in
newdata no longer have to be named after the model
coefficients.prais_winsten returns the new elements
xlevels and contrasts.- Fixed the estimation of models with linearly dependent variables,
which failed with the error “NA/NaN/Inf in ‘x’”. As in
lm,
the coefficients of such variables are set to NA and omitted from the
fitted values, the coefficient table of summary.prais, the
covariance matrices of vcovHC.prais and
vcovPC.prais and from predict.prais.
print.summary.prais reports the number of coefficients
that are not defined because of singularities.- Arguments such as
subset and weights are
now passed on to lm correctly. They could not be evaluated
when they were passed through the dots, which also made the check for
weighted least squares unreachable.
vcovPC.prais throws an informative error if it is used
on a model that was not estimated from panel data.vcovHC.prais and vcovPC.prais no longer
build an n x n matrix, which required a prohibitive amount of memory for
larger samples. The results are unchanged.- The history of the iterations is reported with
message
instead of cat and can be suppressed with
suppressMessages.
- The message about the maximum number of iterations is no longer
shown if rho converged in the last admissible iteration.
prais_winsten throws an error if the variables
specified in argument index are not contained in
data or do not uniquely identify the observations, and
warns if the time variable is not equally spaced.summary.prais returns the residuals of the transformed
model, so that they are on the same scale as the reported residual
standard error.rhoweight no longer fails with “object ‘n_groups’ not
found” if rho is not panel specific. The weights only combine
panel-specific estimates and are ignored otherwise.- Arguments
max_iter and tol are validated.
max_iter = 0 was silently treated as one iteration and made
the estimation of panel-specific rho fail with “subscript out of
bounds”.
- Models whose residuals do not vary, such as a saturated or a
perfectly fitting model, are rejected with an informative error instead
of failing with “length of ‘dimnames’ [2] not equal to array
extent”.
print.summary.prais states that the reported residuals
belong to the transformed model.vcovPC.prais reports panels that do not have a period
in common instead of returning a covariance matrix of NaN. Argument
pairwise = TRUE can be used to match the observations of
two panels by period in that case.prais_winsten reports panels with less than two
observations if panelwise is TRUE, because a
panel-specific rho cannot be obtained from a single observation.- Documentation fixes:
predict.prais has its own help
page, the description of argument ... of
prais_winsten is no longer overwritten, the note on values
of rho above 1 reflects that rho is bounded, summary.prais
documents the components it actually returns, and several typos were
corrected.
prais 1.1.4
- Added the function
predict.prais.
- No calculation of F-statistic in
summary.prais when
only an intercept is estimated.
prais 1.1.3
- Added the
barium example data set.
- Additional check for argument
index of
prais_winsten.
prais 1.1.2
- Fix Lazy Data NOTE issue from CRAN results
- Fix declared imports NOTE issue from CRAN results
- Estimates of rho are bounded to the range [-1, 1] during each
iteration
- Mandatory specification of argument
prais_winsten$index
- Added functionality to estimate panel-specific AR(1)
coefficients
- Added functionality for panel-corrected standard errors (PCSE) with
vcovPC.prais
- Fix typo in
vcovHC.prais documentation
prais 1.1.1
- Add function for semirobust standard errors (#2)
- Add functionality to expand factors to a set of dummy variables
(#1)
- Small fixes in the documentation of prais_winsten() and
summary.prais()
- Changed upper cases in GitHub link to lower cases in
DESCRIPTION
prais 1.1.0
- Added a
NEWS.md file to track changes to the
package
- Added panel data functionality
- Updated documentation
prais 1.0.0
- Added S3 summary and print functions
- Added Durbin-Watson test to summary function
prais 0.0.1