Article

# Testing Panel Data Regression Models with Spatial Error Correlation

Department of Economics, Texas A&M University, College Station, TX 77843-4228, USA
(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. "
##### Working Paper: Bias Correction and Refined Inferences for Fixed Effects Spatial Panel Data Models *
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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.
Full-text · Working Paper · Nov 2015
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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. "
##### Article: Spatio-Temporal Dimensions of Child Poverty in America, 1990-2010
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ABSTRACT: The persistence of childhood poverty in the United States, a wealthy and developed country, continues to pose both an analytical dilemma and public policy challenge, despite many decades of research and remedial policy implementation. In this paper, our goals are twofold, though our primary focus is methodological. We attempt both to examine the relationship between space, time, and previously established factors correlated with childhood poverty at the county level in the continental United States as well as to provide an empirical case study to demonstrate an underutilized methodological approach. We analyze a spatially consistent dataset built from the 1990 and 2000 U.S. Censuses, and the 2006–2010 American Community Survey. Our analytic approach includes cross-sectional spatial models to estimate the reproduction of poverty for each of the reference years as well as a fixed effects panel data model, to analyze change in child poverty over time. In addition, we estimate a full space–time interaction model, which adjusts for spatial and temporal variation in these data. These models reinforce our understanding of the strong regional persistence of childhood poverty in the U.S. over time and suggest that the factors impacting childhood poverty remain much the same today as they have in past decades.
Preview · Article · Aug 2015 · Environment and Planning A
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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. "
##### Article: Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients
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ABSTRACT: We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule-Walker estimation method which applies the least squares estimation to a Yule-Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and {\alpha}-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data.
Full-text · Article · May 2015
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