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A joint serial correlation test for linear panel data models

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  • Yamagata, Takashi

Abstract

This paper proposes a joint error serial correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277-297] and the overidentifying restrictions test. The proposed test, called the test, involves an examination of the joint significance of estimates of second to pth-order (first differenced) error serial correlations. The small sample properties of the test are investigated by means of Monte Carlo experiments. The evidence shows that the proposed test mostly outperforms the conventional m2 test and has high power when the overidentifying restrictions test does not, under a variety of alternatives including slope heterogeneity and cross section dependence.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 146 (2008)
Issue (Month): 1 (September)
Pages: 135-145

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Handle: RePEc:eee:econom:v:146:y:2008:i:1:p:135-145

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Method of moments Dynamic panel data Serial correlation test Slope heterogeneity Cross section dependence m2 test Overidentifying restrictions test;

References

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  1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
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Citations

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Cited by:
  1. Ding, Weili & Lehrer, Steven F., 2014. "Understanding the role of time-varying unobserved ability heterogeneity in education production," Economics of Education Review, Elsevier, vol. 40(C), pages 55-75.
  2. Jianhong Wu & Lixing Zhu, 2012. "Estimation of and testing for random effects in dynamic panel data models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 21(3), pages 477-497, September.
  3. Arbues, Fernando & Villanu´a, Inmaculada & Barberan, Ramon, 2010. "Household size and residential water demand: an empirical approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 54(1), March.
  4. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.

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