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A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator

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Abstract

Panel data characterized by groupwise heteroscedasticity, cross-sectional correlation, and AR(1) serial correlation pose problems for econometric analyses. It is well known that the asymptotically efficient, FGLS estimator (Parks) sometimes performs poorly in finite samples. In a widely cited paper, Beck and Katz (1995) claim that their estimator (PCSE) is able to produce more accurate coefficient standard errors without any loss in efficiency in "practical research situations." This study disputes that claim. We find that the PCSE estimator is usually less efficient than Parks -- and substantially so -- except when the number of time periods is close to the number of cross-sections.

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  • Xiujian Chen & Shu Lin & W. Robert Reed, 2006. "A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator," Working Papers in Economics 06/14, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:06/14
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    1. Michael A. Lapré & Nikos Tsikriktsis, 2006. "Organizational Learning Curves for Customer Dissatisfaction: Heterogeneity Across Airlines," Management Science, INFORMS, vol. 52(3), pages 352-366, March.
    2. Beck, Nathaniel & Katz, Jonathan N., 1995. "What To Do (and Not to Do) with Time-Series Cross-Section Data," American Political Science Review, Cambridge University Press, vol. 89(3), pages 634-647, September.
    3. Joseph P. Dejuan & Maria Jose Luengo‐Prado, 2006. "Consumption and Aggregate Constraints: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(1), pages 81-99, February.
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    1. Moundigbaye, Mantobaye & Messemer, Clarisse & Parks, Richard W. & Reed, W. Robert, 2020. "Bootstrap methods for inference in the Parks model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-18.
    2. Thierry Pénard & Sylvain Dejean & Raphaël Suire, 2011. "Olson’s Paradox Revisited: An Empirical Analysis of Incentives to Contribute in P2P File-sharing Communities," Economics Working Paper Archive (University of Rennes & University of Caen) 201105, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    3. Alonso, José M. & Clifton, Judith & Díaz-Fuentes, Daniel, 2017. "The impact of government outsourcing on public spending: Evidence from European Union countries," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 333-348.
    4. Samet Günay, 2017. "Risk Configuration of S&P 500 Industries: Sigma-risk and Alpha-risk Approximation," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 11(2), pages 196-221, May.
    5. Mehmet Ugur, 2013. "Governance, market power and innovation: evidence from OECD countries," Chapters, in: Mehmet Ugur (ed.), Governance, Regulation and Innovation, chapter 2, pages 25-57, Edward Elgar Publishing.
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    More about this item

    Keywords

    Panel data estimation; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample;
    All these keywords.

    JEL classification:

    • 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

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