IDEAS home Printed from https://ideas.repec.org/p/max/cprwps/136.html

The Hausman-Taylor Panel Data Model with Serial Correlation

Author

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

  • Badi H. Baltagi & Long Liu, 2012. "The Hausman-Taylor Panel Data Model with Serial Correlation," Center for Policy Research Working Papers 136, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:136
    as

    Download full text from publisher

    File URL: https://surface.syr.edu/cpr/194/
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lankoande, Lamoussa Seydou, 2025. "Establishment of mobile money corridors and Burkina Faso's intra-WAEMU trade," Telecommunications Policy, Elsevier, vol. 49(7).
    2. 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.
    3. 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.
    4. Baltagi, Badi H., 2023. "The two-way Hausman and Taylor estimator," Economics Letters, Elsevier, vol. 228(C).
    5. Armando Lenin Támara Ayús & Lina Mar�a Eusse Ossa & Andr�s Castell�n P�rez, 2017. "Efectos del desarrollo financiero sobre el crecimiento económico de Colombia y Chile, 1982-2014," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 9(1), pages 57-67.
    6. 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).
    7. Omgba, Luc Désiré, 2014. "Institutional foundations of export diversification patterns in oil-producing countries," Journal of Comparative Economics, Elsevier, vol. 42(4), pages 1052-1064.
    8. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    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.
    11. Tuğba KAYHAN & Temur KAYHAN & Engin YARBAŞI, 2019. "Profit management in the case of financial distress and global volatile market behaviour: Evidence from Borsa Istanbul Stock Exchange," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(620), A), pages 179-192, Autumn.
    12. Arezoo Ghazanfari, 2022. "What Drives Petrol Price Dispersion across Australian Cities?," Energies, MDPI, vol. 15(16), pages 1-24, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:max:cprwps:136. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katrina Fiacchi (email available below). General contact details of provider: https://edirc.repec.org/data/cpsyrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.