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Specification Tests in Panel Data Models Using Artificial Regressions

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  • Badi H. Baltagi

Abstract

This paper surveys some applications of artificial regressions including the Gauss-Newton, Double-Length and Binary Response Model regressions as testing tools for panel data models. In addition, several other artificial regression tests are reviewed including Hausman's [1978] specification test, Chamberlain's [1982] omnibus goodness-of-fit test and Wooldridge's [1995] simple variable addition tests for selection bias. The important point to emphasize is that in many cases these artificial regressions provide the easiest way to compute specification tests, and in most cases provide a reasonably easy way to do so.

Suggested Citation

  • Badi H. Baltagi, 1999. "Specification Tests in Panel Data Models Using Artificial Regressions," Annals of Economics and Statistics, GENES, issue 55-56, pages 277-297.
    • Handle: RePEc:adr:anecst:y:1999:i:55-56:p:277-297
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    Cited by:

    1. Lee, Yoon-Jin, 2014. "Testing a linear dynamic panel data model against nonlinear alternatives," Journal of Econometrics, Elsevier, vol. 178(P1), pages 146-166.
    2. Erling Häggström, 2002. "Properties of Honda’s test of random individual effects in non-linear regressions," Statistical Papers, Springer, vol. 43(2), pages 177-196, April.
    3. Badi Baltagi & Long Liu, 2014. "Testing for spatial lag and spatial error dependence using double length artificial regressions," Statistical Papers, Springer, vol. 55(2), pages 477-486, May.
    4. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    5. Dilip Nachane & Amlendu Dubey, 2018. "India in the globalized economy : Growth spillovers & business cycle synchronization," International Economics and Economic Policy, Springer, vol. 15(1), pages 89-115, January.

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