rddtools (version 1.6.0)

clusterInf: Post-inference for clustered data

Description

Correct standard-errors to account for clustered data, doing either a degrees of freedom correction or using a heteroskedasticidty-cluster robust covariance matrix possibly on the range specified by bandwidth

Usage

clusterInf(object, clusterVar, vcov. = NULL, type = c("df-adj", "HC"), ...)

Arguments

object

Object of class lm, from which rdd_reg also inherits.

clusterVar

The variable containing the cluster attributions.

vcov.

Specific covariance function to pass to coeftest. See help of sandwich

type

The type of cluster correction to use: either the degrees of freedom, or a HC matrix.

…

Further arguments passed to coeftest

Value

The output of the coeftest function, which is itself of class coeftest

References

Wooldridge (2003) Cluster-sample methods in applied econometrics. AmericanEconomic Review, 93, p. 133-138

See Also

vcovCluster, which implements the cluster-robust covariance matrix estimator used by cluserInf

Examples

# NOT RUN {
data(house)
house_rdd <- rdd_data(y=house$y, x=house$x, cutpoint=0)
reg_para <- rdd_reg_lm(rdd_object=house_rdd)

# here we just generate randomly a cluster variable:
nlet <- sort(c(outer(letters, letters, paste, sep='')))
clusRandom <- sample(nlet[1:60], size=nrow(house_rdd), replace=TRUE)

# now do post-inference:
clusterInf(reg_para, clusterVar=clusRandom)
clusterInf(reg_para, clusterVar=clusRandom, type='HC')
# }