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Bootstrap Methods for Inference in the Parks Model

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Abstract

The Parks (1967) estimator is a workhorse for panel data and seemingly unrelated regression equation systems because it allows the incorporation of serial correlation together with heteroskedasticity and cross-sectional correlation. Unfortunately, the associated, asymptotic standard error estimates are biased downward, often severely. To address this problem, Beck and Katz (1995) developed an approach that uses the Prais-Winsten estimator together with “panel corrected standard errors” (PCSE). While PCSE does not eliminate bias in the estimate of standard errors, its estimates are substantially less biased than Parks. The PCSE estimator has been, and continues to be, widely used. In this paper we develop both parametric and nonparametric bootstrap approaches for inference. We then illustrate the effectiveness of our procedures using Monte Carlo experiments. Up to this point, researchers working with panel datasets where the number of time periods was larger than the number of cross-sections had to sacrifice efficiency (Parks) for accuracy in hypothesis testing (PCSE). The bootstrapping procedures we develop here allow researchers to retain the efficient Parks estimator while producing test results that are superior to the PCSE estimator.

Suggested Citation

  • Mantobaye Moundigbaye & Clarisse Messemer & Richard W. Parks & W. Robert Reed, 2017. "Bootstrap Methods for Inference in the Parks Model," Working Papers in Economics 17/09, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:17/09
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    File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1709.pdf
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    1. Markus Brückner, 2013. "On the simultaneity problem in the aid and growth debate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 126-150, January.
    2. Biagi, Bianca & Brandono, Maria Giovanna & Detotto, Claudio, 2012. "The effect of tourism on crime in Italy: A dynamic panel approach," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 6, pages 1-24.
    3. Xiujian Chen & Shu Lin & W. Robert Reed, 2010. "A Monte Carlo evaluation of the efficiency of the PCSE estimator," Applied Economics Letters, Taylor & Francis Journals, vol. 17(1), pages 7-10, January.
    4. Christian Kleiber & Achim Zeileis, 2010. "The Grunfeld Data at 50," German Economic Review, Verein für Socialpolitik, vol. 11, pages 404-417, November.
    5. Reed W. Robert & Webb Rachel, 2010. "The PCSE Estimator is Good -- Just Not As Good As You Think," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-26, September.
    6. repec:cup:apsrev:v:89:y:1995:i:03:p:634-647_00 is not listed on IDEAS
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    More about this item

    Keywords

    Parks model; PCSE; SUR; panel data; cross-sectional correlation; bootstrap; Monte Carlo; simulation;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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