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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. 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.
    3. Yermack, David, 2006. "Flights of fancy: Corporate jets, CEO perquisites, and inferior shareholder returns," Journal of Financial Economics, Elsevier, vol. 80(1), pages 211-242, April.
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    Cited by:

    1. 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 1 & University of Caen) 201105, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    2. repec:eee:jpolmo:v:39:y:2017:i:2:p:333-348 is not listed on IDEAS
    3. Roberto Fernández Llera & María A. García Valiñas, 2013. "The Role of Regional Public Enterprises in Spain: Room for a Shadow Government?," Hacienda Pública Española, IEF, vol. 205(2), pages 9-31, June.
    4. Mantobaye Moundigbaye & Clarisse Messemer & Richard W. Parks & W. Robert Reed, 2016. "Bootstrap Methods for Inference in the Parks Model," Working Papers in Economics 16/22, University of Canterbury, Department of Economics and Finance.
    5. José M. Alonso & Judith Clifton & Daniel Díaz-Fuentes, 2015. "Did New Public Management Matter? An empirical analysis of the outsourcing and decentralization effects on public sector size," Public Management Review, Taylor & Francis Journals, vol. 17(5), pages 643-660, May.
    6. Roberto Fernández Llera & María A. García Valiñas, 2010. "Efficiency and elusion: both sides of public enterprises in Spain," Working Papers 2010/5, Institut d'Economia de Barcelona (IEB).

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    Keywords

    Panel data estimation; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample;

    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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