Testing Panel Data Regression Models with Spatial Error Correlation

4 CONCLUSION

It is clear from the extensive Monte Carlo experiments performed that the spatial economet-
rics literature should not ignore the heterogeneity across cross-sectional units when testing
for the presence of spatial error correlation. Similarly, the panel data econometrics literature
should not ignore the spatial error correlation when testing for the presence of random re-
gional effects. Both joint and conditional LM tests have been derived in this paper that are
easy to implement and that perform better in terms of size and power than the one-directional
LM tests. The latter tests ignore the random regional effects when testing for spatial error
correlation or ignore spatial error correlation when testing for random regional effects. This
paper does not consider testing for spatial lag dependence and random regional effects in a
panel. This should be the subject of future research. Also, the results in the paper should
be tempered by the fact that the N = 25; 49 used in our Monte Carlo experiments may be
small for a typical micro panel. Larger N will probably improve the performance of these
tests whose critical values are based on their large sample distributions.  However, it will

also increase the computation di∏icnlty and accuracy of the eigenvalues of the big weighting
matrix W. Finally, it is important to point out that the asymptotic distribution of our test
statistics were not explicitly derived in the paper but that they are likely to hold under a
similar set of low level assumptions developed by Kelejian and Prucha (2001).

5 REFERENCES

Anselin, L. (1988). Spatial Econometrics: Methods and Models (Kluwer Academic Publish-
ers, Dordrecht).

Anselin, L. (1999). Rao’s score tests in spatial econometrics. Journal of Statistical Planning
and Inference, (forthcoming).

Anselin, L. and A.K. Bera (1998). Spatial dependence in linear regression models with an
introduction to spatial econometrics. In A. Ullah and D.E.A. Giles, (eds.), Handbook
of Applied Economic Statistics, Marcel Dekker, New York.

Anselin, L. , A.K. Bera, R. Florax and M.J. Yoon (1996). Simple diagnostic tests for spatial
dependence. Regional Science and Urban Economics 26, 77-104.

Anselin, L and S. Rey (1991). Properties of tests for spatial dependence in linear regression
models. Geographical Analysis 23, 112-131.

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