vcovCR.plm {clubSandwich}  R Documentation 
vcovCR
returns a sandwich estimate of the variancecovariance matrix
of a set of regression coefficient estimates from a plm
object.
## S3 method for class 'plm'
vcovCR(
obj,
cluster,
type,
target,
inverse_var,
form = "sandwich",
ignore_FE = FALSE,
...
)
obj 
Fitted model for which to calculate the variancecovariance matrix 
cluster 
Optional character string, expression, or vector indicating
which observations belong to the same cluster. For fixedeffect 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 specifying the name of
the individual index (e.g., 
type 
Character string specifying which smallsample adjustment should
be used, with available options 
target 
Optional matrix or vector describing the working
variancecovariance model used to calculate the 
inverse_var 
Optional logical indicating whether the weights used in
fitting the model are inversevariance. If not specified, 
form 
Controls the form of the returned matrix. The default

ignore_FE 
Optional logical controlling whether fixed effects are ignored when calculating smallsample 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.
library(plm)
# fixed effects
data("Produc", package = "plm")
plm_FE < plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state","year","region"),
effect = "individual", model = "within")
vcovCR(plm_FE, type="CR2")
vcovCR(plm_FE, type = "CR2", cluster = Produc$region) # clustering on region
# random effects
plm_RE < update(plm_FE, model = "random")
vcovCR(plm_RE, type = "CR2")
vcovCR(plm_RE, type = "CR2", cluster = Produc$region) # clustering on region
# nested random effects
plm_nested < update(plm_FE, effect = "nested", model = "random")
vcovCR(plm_nested, type = "CR2") # clustering on region
# first differencing
data(Fatalities, package = "AER")
Fatalities < within(Fatalities, {
frate < 10000 * fatal / pop
drinkagec < cut(drinkage, breaks = 18:22, include.lowest = TRUE, right = FALSE)
drinkagec < relevel(drinkagec, ref = 4)
})
plm_FD < plm(frate ~ beertax + drinkagec + miles + unemp + log(income),
data = Fatalities, index = c("state", "year"),
model = "fd")
vcovHC(plm_FD, method="arellano", type = "sss", cluster = "group")
vcovCR(plm_FD, type = "CR1S")
vcovCR(plm_FD, type = "CR2")