vcovCR returns a sandwich estimate of the variance-covariance matrix
of a set of regression coefficient estimates from a plm
object.
# S3 method for plm
vcovCR(obj, cluster, type, target, inverse_var,
form = "sandwich", ignore_FE = FALSE, ...)Fitted model for which to calcualte the variance-covariance matrix
Optional character string, expression, or vector indicating
which observations belong to the same cluster. For fixed-effect models that
include individual effects or time effects (but not both), the cluster will
be taken equal to the included fixed effects if not otherwise specified.
Clustering on individuals can also be obtained by taking cluster =
"individual" and clustering on time periods can be obtained with
cluster = "time". For random-effects models, the cluster will be
taken equal to the included random effect identifier if not otherwise
specified.
Character string specifying which small-sample adjustment should be used.
Optional matrix or vector describing the working
variance-covariance model used to calculate the CR2 and CR4
adjustment matrices. By default, the target is taken to be an identity
matrix for fixed effect models or the estimated compound-symmetric covariance matrix for random effects models.
Optional logical indicating whether the weights used in
fitting the model are inverse-variance. If not specified, vcovCR
will attempt to infer a value.
Controls the form of the returned matrix. The default
"sandwich" will return the sandwich variance-covariance matrix.
Alternately, setting form = "meat" will return only the meat of the
sandwich and setting form = B, where B is a matrix of
appropriate dimension, will return the sandwich variance-covariance matrix
calculated using B as the bread.
Optional logical controlling whether fixed effects are ignored when calculating small-sample adjustments in models where fixed effects are estimated through absorption.
Additional arguments available for some classes of objects.
An object of class c("vcovCR","clubSandwich"), which consists
of a matrix of the estimated variance of and covariances between the
regression coefficient estimates.