Testing for serial correlation, spatial autocorrelation and random effects using panel data

• Published in: Journal of econometrics 2007-09, Vol.140 (1), p.5-51
• Main Authors: Baltagi, Badi H., Heun Song, Seuck, Cheol Jung, Byoung, Koh, Won
• Format: Article
• Language: English
• Subjects: Correlation; Correlation analysis; Econometrics; Error; Lagrange multiplier; Lagrange multiplier tests; Likelihood ratio tests; Monte Carlo simulation; Panel data; Regression analysis; Serial correlation; Spatial error correlation; Spatial models; Studies
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2006.09.001

This paper considers a spatial panel data regression model with serial correlation on each spatial unit over time as well as spatial dependence between the spatial units at each point in time. In addition, the model allows for heterogeneity across the spatial units using random effects. The paper then derives several Lagrange multiplier tests for this panel data regression model including a joint test for serial correlation, spatial autocorrelation and random effects. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin and Bera [1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah, A., Giles, D.E.A. (Eds.), Handbook of Applied Economic Statistics. Marcel Dekker, New York] and in the panel data context by Baltagi et al. [2003. Testing panel data regression models with spatial error correlation. Journal of Econometrics 117, 123–150]. The second is the LM tests for the error component panel data model with serial correlation derived by Baltagi and Li [1995. Testing AR(1) against MA(1) disturbances in an error component model. Journal of Econometrics 68, 133–151]. Hence, the joint LM test derived in this paper encompasses those derived in both strands of earlier works. In fact, in the context of our general model, the earlier LM tests become marginal LM tests that ignore either serial correlation over time or spatial error correlation. The paper then derives conditional LM and LR tests that do not ignore these correlations and contrast them with their marginal LM and LR counterparts. The small sample performance of these tests is investigated using Monte Carlo experiments. As expected, ignoring any correlation when it is significant can lead to misleading inference.