Article

Testing Panel Data Regression Models with Spatial Error Correlation

Department of Economics, Texas A&M University, College Station, TX 77843-4228, USA
Journal of Econometrics (Impact Factor: 1.6). 02/2003; 117(1):123-150. DOI: 10.1016/S0304-4076(03)00120-9
Source: RePEc

ABSTRACT

This paper derives several lagrange multiplier (LM) tests for the panel data regression model with spatial error correlation. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin (Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht; Rao's score test in spatial econometrics, J. Statist. Plann. Inference 97 (2001) 113) and Anselin et al. (Regional Sci. Urban Econom. 26 (1996) 77), and the second is the LM tests for the error component panel data model discussed in Breusch and Pagan (Rev. Econom. Stud. 47(1980) 239) and Baltagi et al. (J. Econometrics 54 (1992) 95). The idea is to allow for both spatial error correlation as well as random region effects in the panel data regression model and to test for their joint significance. Additionally, this paper derives conditional LM tests, which test for random regional effects given the presence of spatial error correlation. Also, spatial error correlation given the presence of random regional effects. These conditional LM tests are an alternative to the one-directional LM tests that test for random regional effects ignoring the presence of spatial error correlation or the one-directional LM tests for spatial error correlation ignoring the presence of random regional effects. We argue that these joint and conditional LM tests guard against possible misspecification. Extensive Monte Carlo experiments are conducted to study the performance of these LM tests as well as the corresponding likelihood ratio tests.

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Available from: Badi H. Baltagi
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    • "Popular methods of model estimation and inferences are quasi maximum likelihood (QML) and generalized method of moments (GMM). SeeYu (2010a, 2015a) andAnselin et al. (2008)Baltagi et al. (2003, 2013), Kapoor et al. (2007), Yu et al. (2008, 2012), Yu and Lee (2010), Lee and Yu (2010a,b), Baltagi and Yang (2013a,b), and Su and Yang (2015).Bao, 2013;Yang, 2015), and more so with a denser spatial weight matrix (Yang, 2015;Liu and Yang, 2015a). As a result the subsequent model inferences (based on t-ratios) can be seriously affected. "
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    DESCRIPTION: This paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the proposed t-ratios are much more reliable than the usual $t$-ratios.
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    • "However, the field of spatial econometrics has matured considerably over the past three decades (Anselin, 2010). Recent methodological advances include spatiotemporal econometric modeling (Elhorst, 2003; LeSage and Pace, 2009: Chapter 7) and formal panel studies explicitly incorporating spatial effects (Baltagi et al., 2003). These developments are the methodological focus of the present analysis. "
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    • "See, e.g. Lee and Yu (2010a). Baltagi et al. (2003) considers a static spatial panel model where the error term is a SAR model. Xu and Lee (2010) shows that the maximum likelihood estimator is inconsistent when heteroskedastisity exists in the error term for static panel data models and proposes an alternative GMM estimation method. "
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