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The PCSE Estimator is Good -- Just Not as Good as You Think

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Abstract

This paper investigates the properties of the Panel-Corrected Standard Error (PCSE) estimator. The PCSE estimator is commonly used when working with time-series, crosssectional (TSCS) data. In an influential paper, Beck and Katz (1995) (henceforth BK) demonstrated that FGLS produces coefficient standard errors that are severely underestimated. They report Monte Carlo experiments in which the PCSE estimator produces accurate standard error estimates at no, or little, loss in efficiency compared to FGLS. Our study further investigates the properties of the PCSE estimator. We first reproduce the main experimental results of BK using their Monte Carlo framework. We then show that the PCSE estimator does not perform as well when tested in data environments that better resemble “practical research situations.” When (i) the explanatory variable(s) are characterized by substantial persistence, (ii) there is serial correlation in the errors, and (iii) the time span of the data series is relatively short, coverage rates for the PCSE estimator frequently fall between 80 and 90 percent. Further, we find many “practical research situations” where the PCSE estimator compares poorly with FGLS on efficiency grounds.

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  • W. Robert Reed & Rachel Webb, 2010. "The PCSE Estimator is Good -- Just Not as Good as You Think," Working Papers in Economics 10/53, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:10/53
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    File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1053.pdf
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    1. Andrew K. Rose, 2004. "Do We Really Know That the WTO Increases Trade?," American Economic Review, American Economic Association, pages 98-114.
    2. Noy, Ilan, 2009. "The macroeconomic consequences of disasters," Journal of Development Economics, Elsevier, pages 221-231.
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    4. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," Review of Financial Studies, Society for Financial Studies, pages 435-480.
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    Cited by:

    1. Mantobaye Moundigbaye & William Rea & W. Robert Reed, 2016. "More Evidence On “Which Panel Data Estimator Should I Use?”," Working Papers in Economics 16/18, University of Canterbury, Department of Economics and Finance.
    2. repec:spr:laecrv:v:26:y:2017:i:1:d:10.1007_s40503-017-0053-6 is not listed on IDEAS
    3. Jones, Sam, 2015. "Aid Supplies Over Time: Addressing Heterogeneity, Trends, and Dynamics," World Development, Elsevier, vol. 69(C), pages 31-43.
    4. Gu, Lulu & Reed, W. Robert, 2013. "Information asymmetry, market segmentation, and cross-listing: Implications for event study methodology," Journal of Asian Economics, Elsevier, pages 28-40.
    5. V. Saravanakumar, "undated". "Impact of Climate Change on Yield of Major Food Crops in Tamil Nadu, India," Working papers 91, The South Asian Network for Development and Environmental Economics.
    6. Arestis, Philip & Gonzalez-Martinez, Ana Rosa, 2016. "Revisiting the accelerator principle in a world of uncertainty: Some empirical evidence," Economic Modelling, Elsevier, vol. 56(C), pages 35-42.
    7. 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.
    8. Mantobaye Moundigbaye & William S. Rea & W. Robert Reed, 2017. "Which Panel Data Estimator Should I Use?: A Corrigendum and Extension," Working Papers in Economics 17/10, University of Canterbury, Department of Economics and Finance.
    9. Reed, W. Robert & Webb, Rachel S., 2011. "Estimating standard errors for the Parks model: Can jackknifing help?," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 5, pages 1-14.
    10. 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.
    11. Tomas Konecny & Jakub Seidler & Aelta Belyaeva & Konstantin Belyaev, 2017. "The Time Dimension of the Links Between Loss Given Default and the Macroeconomy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(6), pages 462-491, October.
    12. Yoon, Jangho & Luck, Jeff, 2016. "Intersystem return on investment in public mental health: Positive externality of public mental health expenditure for the jail system in the U.S," Social Science & Medicine, Elsevier, pages 133-142.
    13. Lulu Gu & W.R. Reed, 2013. "Chinese overseas M&A performance and the Go Global policy," The Economics of Transition, The European Bank for Reconstruction and Development, pages 157-192.

    More about this item

    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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