Cluster-robust standard errors using R - CiteSeerX
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Cluster-robust standard errors using R - CiteSeerX
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Cluster-robust standard errors using R Mahmood Arai Department of Economics and SULCIS Stockholm University January 31, 2011
1
Introduction
This note deals with estimating cluster-robust standard errors on one and two dimensions using R (see R Development Core Team [2007]). Cluster-robust standard errors are an issue when the errors are correlated within groups of observations. For discussion of robust inference under within groups correlated errors, see Wooldridge [2003], Cameron et al. [2006], and Petersen [2005] and the references therein. Two data sets are used. The first data set is panel data from Introduction to Econometrics by Stock and Watson [2006a], chapter 10. The second data set is the Mitchell Petersen’s test data for two-way clustering. The first part of this note deals with estimation of fixed-effects model using the Fatality data. The second part deals with cluster-robust standard errors. You need to install package lmtest by Torsten Hothorn, Achim Zeileis, Giovanni Millo and David Mitchell, package sandwich by Thomas Lumley and Achim Zeileis, package plm by Yves Croissant and Giovanni Millo and Ecdat by Yves Croissant. The function robcov in the package Design by Frank E. Harrell Jr can be used for clustering in one dimension in case of an ols-fit. The function plm can be used for obtaining one-way clustered standard errors.
2
Estimating fixed-effects model
The data set Fatality in the package Ecdat cover data for 48 US states over 7 years. One way to estimate such a model is to include fixed group intercepts in the model. This is an example estimating a two-way fixed effects model. > library(Ecdat) > data(Fatality) > LSDV gcenter
library(sandwich) M library(plm) [1] "kinship is loaded" > fmplm clx
