Panel Data Inference Under Spatial Dependence
Badi H. Baltagi
Syracuse University - Center for Policy Research
ERMES (CNRS), Université Panthéon-Assas Paris II; INRETS-DEST
March 1, 2010
Center for Policy Research Working Paper No. 123
This paper focuses on inference based on the usual 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 auto-regressive 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 usual panel data estimators that ignore spatial dependence can lead to misleading inference.
Number of Pages in PDF File: 38
Keywords: Panel data, Hausman test, Random effect, Spatial autocorrelation, Maximum likelihood
JEL Classification: C33working papers series
Date posted: April 10, 2011
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