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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

Suggested Citation

  • 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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    Cited by:

    1. Chen, Jing & Yue, Rongxian & Wu, Jianhong, 2020. "Testing for individual and time effects in the two-way error component model with time-invariant regressors," Economic Modelling, Elsevier, vol. 92(C), pages 216-229.
    2. 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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    4. Dhaene, Geert & Zhu, Yu, 2017. "Median-based estimation of dynamic panel models with fixed effects," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 398-423.
    5. 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.
    6. Harding, Matthew & Lamarche, Carlos, 2014. "A Hausman–Taylor instrumental variable approach to the penalized estimation of quantile panel models," Economics Letters, Elsevier, vol. 124(2), pages 176-179.
    7. Aquaro, M. & Čížek, P., 2013. "One-step robust estimation of fixed-effects panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 536-548.
    8. Yvonne Umulisa, 2020. "Estimation of the East African Community's trade benefits from promoting intra‐regional trade," African Development Review, African Development Bank, vol. 32(1), pages 55-66, March.
    9. Amoroso, Sara & Bruno, Randolph Luca & Magazzini, Laura, 2022. "The Identification of Time-Invariant Variables in Panel Data Model: Exploring the Role of Science in Firms’ Productivity," IZA Discussion Papers 15708, Institute of Labor Economics (IZA).
    10. Schorr, A. & Lips, M., 2018. "Influence of milk yield on profitability a quantile regression analysis," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277000, International Association of Agricultural Economists.
    11. Liouaeddine, Mariem & Bijou, Mohammed & Naji, Faïrouz, 2017. "The Main Determinants of Moroccan Students' Outcomes," MPRA Paper 80247, University Library of Munich, Germany.
    12. 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.
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    14. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.

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    Keywords

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