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A Robust Hausman-Taylor Estimator

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Abstract

This paper suggests a robust Hausman and Taylor (1981) estimator, here-after HT, that deals with the possible presence of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007) robust Within MS estimator instead of the Within estimator in the first stage of the HT estimator. The second modification uses the robust Wagenvoort and Waldmann (2002) two stage generalized MS estimator instead of the 2SLS estimator in the second step of the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust HT estimator yields large gains in MSE as compared to its classical Hausman-Taylor counterpart. We illustrate this robust version of the Hausman-Taylor estimator using an empirical application. Key Words: Bad leverage points, Hausman-Taylor, panel data, two stage generalized MS estimator, vertical outliers. JEL No. C23, C26

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  • Badi H. Baltagi & Georges Bresson, 2012. "A Robust Hausman-Taylor Estimator," Center for Policy Research Working Papers 140, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:140
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    4. 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.
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    7. M. Hashem Pesaran & Qiankun Zhou, 2018. "Estimation of time-invariant effects in static panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1137-1171, November.
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    More about this item

    Keywords

    bad leverage points; hausman-taylor; panel data; two stage generalized ms estimator; vertical outliers. jel no. c23; c26;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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