Heteroskedasticity, temporal and spatial correlation matter

Abstract

As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang’s (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang’s (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed effects. Our Monte Carlo experiment shows that the OLS exhibits an important downward bias in all of the cases and almost always has the worst performance when compared to the other estimators. The GLS corrected for HACSC performs well if time dimension is greater than cross-sectional dimension. The best performance can be attributed to the Vogelsang’s estimator with fixed-b version of Driscoll-Kraay standard errors.