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The Hausman–Taylor panel data model with serial correlation

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  • Baltagi, Badi H.
  • Liu, Long

Abstract

This paper modifies the Hausman and Taylor (1981) panel data estimator to allow for serial correlation in the remainder disturbances. It demonstrates the gains in efficiency of this estimator versus the standard panel data estimators that ignore serial correlation using Monte Carlo experiments.

Suggested Citation

  • Baltagi, Badi H. & Liu, Long, 2012. "The Hausman–Taylor panel data model with serial correlation," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1401-1406.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:7:p:1401-1406
    DOI: 10.1016/j.spl.2012.03.016
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    1. A. Bhargava & L. Franzini & W. Narendranathan, 2006. "Serial Correlation and the Fixed Effects Model," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 4, pages 61-77, World Scientific Publishing Co. Pte. Ltd..
    2. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    3. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2003. "Fixed effects, random effects or Hausman-Taylor?: A pretest estimator," Economics Letters, Elsevier, vol. 79(3), pages 361-369, June.
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    6. So Im, Kyung & Ahn, Seung C. & Schmidt, Peter & Wooldridge, Jeffrey M., 1999. "Efficient estimation of panel data models with strictly exogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 93(1), pages 177-201, November.
    7. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
    8. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    9. Yongcheol Shin & Laura Serlenga, 2007. "Gravity models of intra-EU trade: application of the CCEP-HT estimation in heterogeneous panels with unobserved common time-specific factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 361-381.
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    6. Tsakiridis, Andreas & Breen, James & O'Donoghue, Cathal & Hanrahan, Kevin & Wallace, Michael & Crosson, Paul, 2016. "Flexibility of beef suckler cow systems under varying calf retention strategies," 90th Annual Conference, April 4-6, 2016, Warwick University, Coventry, UK 236289, Agricultural Economics Society.
    7. Yashobanta Parida & Joyita Roy Chowdhury, 2025. "Effects of election and natural disaster mortality on calamity relief spending in India," Economics of Governance, Springer, vol. 26(1), pages 93-138, March.
    8. Shobande, Olatunji A. & Ogbeifun, Lawrence, 2023. "Pooling cross-sectional and time series data for estimating causality between technological innovation, affluence and carbon dynamics: A comparative evidence from developed and developing countries," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    9. Tatjana Brankov & Bojan Matkovski & Marija Jeremić & Stanislav Zekić, 2022. "GMO standards in South East Europe: assessing a GMO index within the process of EU integration," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(1), pages 253-275, February.
    10. Rodríguez-Pose, Andrés & Vidal-Bover, MIquel, 2022. "Unfunded mandates and the economic impact of decentralisation. When finance does not follow function," CEPR Discussion Papers 17613, C.E.P.R. Discussion Papers.
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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