Heteroskedasticity, Autocorrelation, and Spatial Correlation Robust Inference in Linear Panel Models with Fixed Effects

• Activity Number: 150
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2009 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Tim Vogelsang*+
• Companies: Michigan State University
• Keywords: Panel Regression ; Fixed-b asymptotics ; Robust Standard Errors ; HAC estimator ; Spatial Correlation ; Fixed Effects

Abstract

This paper develops an asymptotic theory for test statistics in linear panel models that are robust to heteroskedasticity, autocorrelation and/or spatial correlation. Two classes of standard errors are analyzed. Both are based on kernel heteroskedasticity autocorrelation (HAC) covariance matrix estimators. The first class uses averages of HAC estimates across individuals in the cross-section, i.e. "averages of HACs." The second class uses the HAC of cross-section averages. The "HAC of averages" standard errors are robust to heteroskedasticity, serial correlation and spatial correlation but stationarity in the time dimension is required. The main contribution of the paper is to develop a fixed-b asymptotic theory under large-T/fixed-n and large-T,n for statistics based on both classes of standard errors in models with individual and possibly time fixed-effects dummy variables.