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A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator

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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.
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    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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