Panel Data Inference under Spatial Dependence
AbstractThis 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.
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Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 123.
Length: 38 pages
Date of creation: Mar 2010
Date of revision:
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More information through EDIRC
Panel data; Hausman test; Random effect; Spatial autocorrelation; Maximum Likelihood.;
Other versions of this item:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-05-02 (All new papers)
- NEP-ECM-2010-05-02 (Econometrics)
- NEP-GEO-2010-05-02 (Economic Geography)
- NEP-URE-2010-05-02 (Urban & Real Estate Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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