Panel data inference under spatial dependence

Badi H. Baltagi, Alain Pirotte

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

This paper focuses on inference based on the standard panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial autoregressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the standard panel data estimators that ignore spatial dependence can lead to misleading inference.

Original languageEnglish (US)
Pages (from-to)1368-1381
Number of pages14
JournalEconomic Modelling
Volume27
Issue number6
DOIs
StatePublished - Nov 2010

Keywords

  • Hausman test
  • Maximum likelihood
  • Panel data
  • Random effect
  • Spatial autocorrelation

ASJC Scopus subject areas

  • Economics and Econometrics

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