Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data

1998/11/01 by John C. Driscoll, Aart Kraay, Aart C. Kraay · 47 citations
Economics, Econometrics and Finance · #Spatial and Panel Data Analysis #Regional Economics and Spatial Analysis #Fiscal Policy and Economic Growth

paper · doi:10.1162/003465398557825

Abstract

Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.

Citations

• Large Sample Properties of Generalized Method of Moments Estimators
• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• The overvaluation of purchasing power parity

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