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: 1.53). 02/2003; 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.

Download full-text


Available from: Badi H. Baltagi, Jun 17, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We empirically investigate the determinants of local authority mental health expenditure in England. We adopt a reduced form demand and supply model, extended to incorporate possible interaction among authorities, as well as unobserved heterogeneity. The model is estimated using an annual panel dataset that allows us to explore both time-series and cross-municipality variation in mental health expenditure. Results are consistent with some degree of interdependence between neighbouring municipalities in spending decisions. This first attempt to apply spatial panels in investigating health expenditure offers insights and raises new questions.
    Journal of Health Economics 08/2007; 26(4):842-64. DOI:10.1016/j.jhealeco.2006.12.008 · 2.25 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Most of the existing literature on panel data cointegration assumes cross-sectional independence, an assumption that is difficult to satisfy. This paper studies panel cointegration under cross-sectional dependence, which is characterized by a factor structure. We derive the limiting distribution of a fully modified estimator for the panel cointegrating coefficients. We also propose a continuous-updated fully modified (CUP-FM) estimator). Monte Carlo results show that the CUP-FM estimator has better small sample properties than the two-step FM (2S-FM) and OLS estimators.
    SSRN Electronic Journal 01/2006; DOI:10.2139/ssrn.1815227
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article investigates spatial panel data models with a space–time filter in disturbances. We consider their estimation by both fixed effects and random effects specifications. With a between equation properly defined, the difference of the random versus fixed effects models can be highlighted. We show that the random effects estimate is a pooling of the within and between estimates. A Hausman‐type specification test and an Lagrangian multiplier test are proposed for the testing of the random components specification versus the fixed effects specification.
    International Economic Review 11/2012; 53(4). DOI:10.1111/j.1468-2354.2012.00724.x · 1.56 Impact Factor