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Unformatted text preview: Spatial panels: random components vs. &xed e/ects Lung-fei Lee Department of Economics Ohio State University email@example.com Jihai Yu Department of Economics University of Kentucky firstname.lastname@example.org January 8, 2010 Abstract This paper investigates spatial panel data models with spatially and serially correlated disturbances. We &rst discuss the short panels and consider their estimation by both &xed e/ects and random e/ects speci&cations. With a between equation properly de&ned, the di/erence of the random vs. &xed e/ects models can be highlighted. We show that the random e/ects estimate is a pooling of the within and between estimates. A Hausman type speci&cation test and also an LM test are proposed for the testing of the random components speci&cation vs. the &xed e/ects speci&cation. We then discuss the case for long panels with time e/ects included. After the time e/ects are eliminated, we develop the &xed e/ects and random e/ects estimates, and show that the within estimate is asymptotically as e cient as the random e/ects estimate. JEL classi&cation: C13; C23; R15 Keywords: Spatial autoregression, Panel data, Random components, Fixed e/ects, Maximum likeli- hood estimation, Pooling 1 Introduction Panel data with spatial interactions is of interest as it enables researchers to take into account the dynamic and spatial dependence and also control for the unobservable heterogeneity. Anselin (1988) provides a panel regression model with error components and spatial autoregressive (SAR) disturbances. Baltagi et al. (2003) consider the speci&cation test for spatial correlation in that panel regression model. Kapoor et al. (2007) have a di/erent speci&cation of error components and SAR structure in the overall disturbance and suggest a method of moments (MOM) estimation of their model; and Fingleton (2008) adopts a similar approach to estimate a spatial panel model with SAR dependent variables but with random components and a spatial moving average (SMA) structure in the overall disturbance. In an attempt to nest the Anselin (1988) and Kapoor et al. (2007) models, Baltagi et al. (2007a) suggest an extended model. As an alternative to the random e/ects speci&cation, Lee and Yu (2010) consider the &xed e/ects speci&cation. The &xed e/ects model has the advantage of robustness in that the &xed e/ects are allowed to depend on included regressors in the model. It can also provide a uni&ed model framework as the di/erent random e/ects models in Anselin (1988), Kapoor et al. (2007) and Baltagi et al. (2007a) reduce to the same &xed e/ects model, when the random e/ects are conditioned upon as &xed parameters. Lee and Yu (2010) investigate the estimation of &xed e/ects spatial panel data models with a partial likelihood approach, where the &xed e/ects may also include time e/ects in addition to individual e/ects....
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This note was uploaded on 12/26/2011 for the course ECON 245a taught by Professor Staff during the Fall '08 term at UCSB.
- Fall '08