Testing Panel Data Regression Models with Spatial Error Correlation

Provided by Research Papers in Economics

Testing Panel Data Regression Models with Spatial Error
Correlation*

by

Badi H. Baltagi

Department of Economics, Texas A&M University,
College Station, Texas 77843-4228, USA
(979) 845-7380
badi@econ.tamu.edu

Seuck Heun Song
and

Won Koh

Department of Statistics, Korea University,
Sungbuk-Ku, Seoul, 136-701, Korea
ssong@mail.korea.ac.kr

wonkoh@kustat.korea.ac.kr

December 2001

Keywords: Panel data; Spatial error correlation, Lagrange Multiplier tests, Likelihood Ratio tests.

JEL classification: C23, C12

ABSTRACT

This paper derives several Lagrange Multiplier tests for the panel data regression model wih 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 (1988, 1999) and Anselin, Bera, Florax and Yoon
(1996), and the second is the LM tests for the error component panel data model discussed in Breusch
and Pagan (1980) and Baltagi, Chang and Li (1992).  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.

*We would like to thank the associate editor and two referees for helpful comments. An earlier version of this
paper was given at the North American Summer Meeting of the Econometric Society held at the University
of Maryland, June, 2001.  Baltagi would like to thank the Bush School Program in the Economics of Public

Policy for its financial support.

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