Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data

1998/11/01 by John C. Driscoll, Aart Kraay, Aart C. Kraay · 6,469 citations
Economics, Econometrics and Finance · Mathematics · #Computer science #Covariance #Covariance matrix #Dimension (graph theory) #Econometrics #Economics #Estimation #Extension (predicate logic) #Fiscal Policy and Economic Growth #Mathematics #Matrix (chemical analysis) #Monte Carlo method #Nonparametric statistics #Panel data #Regional Economics and Spatial Analysis #Spatial and Panel Data Analysis #Spatial dependence #Standard error #Statistics

paper · doi:10.1162/003465398557825

published in The Review of Economics and Statistics 80(4), 549-560 (The MIT Press)

openalex publication_date 1998/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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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