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Panel data inference under spatial dependence
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DOI：10.1016/j.econmod.2010.07.004.png)
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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. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Panel data
Hausman test
Random effect
Spatial autocorrelation
Maximum likelihood
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