Panel data inference under spatial dependence
AbstractThis 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 27 (2010)
Issue (Month): 6 (November)
Contact details of provider:
Web page: http://www.elsevier.com/locate/inca/30411
Panel data Hausman test Random effect Spatial autocorrelation Maximum likelihood;
Other versions of this item:
- Badi H. Baltagi & Alain Pirotte, 2010. "Panel Data Inference under Spatial Dependence," Center for Policy Research Working Papers 123, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi B-H. & Pirotte A., 2009. "Panel Data Inference Under Spatial Dependence," Working Papers ERMES 0907, ERMES, University Paris 2.
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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