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Testing the fixed effects restrictions? A Monte Carlo study of Chamberlain’s Minimum Chi-Squared test

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  • BALTAGI H-B
  • BRESSON G.
  • PIROTTE A.

Abstract

Chamberlain [Chamberlain, G., 1982. Multivariate regression models for panel data. Journal of Econometrics 18, 5-46] showed that the fixed effects (FE) specification imposes testable restrictions on the coefficients from regressions of all leads and lags of dependent variables on all leads and lags of independent variables. Angrist and Newey [Angrist, J.D., Newey, W.K., 1991. Over-identification tests in earnings functions with fixed effects, Journal of Business & Economic Statistics 9, 317-323] suggested computing this test statistic as the degrees of freedom times the R2 from a regression of within residuals on all leads and lags of the exogenous variables. Despite the simplicity of these tests, they are not commonly used in practice. Instead, a Hausman [Hausman, J.A., 1978. Specification tests in econometrics, Econometrica 46, 1251-1271] test is used based on a contrast of the fixed and random effects specifications. We advocate the use of Chamberlain test if the researcher wants to settle on the FE specification and we check this test's performance using Monte Carlo experiments and we apply it to the crime example of Cornwell and Trumbull [Cornwell, C., Trumbull, W.N., 1994. Estimating and economic model of crime with panel data. Review of Economics and Statistics 76, 360-366].
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  • Baltagi H-B & Bresson G. & Pirotte A., 2007. "Testing the fixed effects restrictions? A Monte Carlo study of Chamberlain’s Minimum Chi-Squared test," Working Papers ERMES 0703, ERMES, University Paris 2.
    • Handle: RePEc:erm:papers:0703
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    References listed on IDEAS

    as
    1. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Owusu-Gyapong, Anthony, 1986. "Alternative Estimating Techniques for Panel Data on Strike Activity," The Review of Economics and Statistics, MIT Press, vol. 68(3), pages 526-531, August.
    4. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    5. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    6. Cardellichio, Peter A, 1990. "Estimation of Production Behavior Using Pooled Microdata," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 11-18, February.
    7. Angrist, Joshua D & Newey, Whitney K, 1991. "Over-Identification Tests in Earnings Functions with Fixed Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 317-323, July.
    8. Bowsher, Clive G., 2002. "On testing overidentifying restrictions in dynamic panel data models," Economics Letters, Elsevier, vol. 77(2), pages 211-220, October.
    9. Cornwell, Christopher & Trumbull, William N, 1994. "Estimating the Economic Model of Crime with Panel Data," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 360-366, May.
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    Cited by:

    1. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2018. "Robust linear static panel data models using ε-contamination," Journal of Econometrics, Elsevier, vol. 202(1), pages 108-123.
    2. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2014. "Robust linear static panel data models using epsilon-contamination," MPRA Paper 59896, University Library of Munich, Germany.
    3. Pedro Araujo & Olena Mykhaylova & James Staveley-O’Carroll, 2015. "Financial liberalization and patterns of international portfolio holdings," Empirical Economics, Springer, vol. 49(1), pages 213-234, August.

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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