Panel Data Inference under Spatial Dependence
Badi Baltagi () and
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Alain Pirotte: ERMES (CNRS) and TEPP (CNRS), Université Panthéon-Assas Paris II, France INRETS-DEST, National Institute of Research on Transports and Safety, France
No 123, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University
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.
Keywords: Panel data; Hausman test; Random effect; Spatial autocorrelation; Maximum Likelihood. (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
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Journal Article: Panel data inference under spatial dependence (2010)
Working Paper: Panel Data Inference Under Spatial Dependence (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:123
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