IDEAS home Printed from https://ideas.repec.org/p/cbt/econwp/10-53.html
   My bibliography  Save this paper

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

Author

Listed:

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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://repec.canterbury.ac.nz/cbt/econwp/1053.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    2. Andrew K. Rose, 2004. "Do We Really Know That the WTO Increases Trade?," American Economic Review, American Economic Association, vol. 94(1), pages 98-114, March.
    3. Noy, Ilan, 2009. "The macroeconomic consequences of disasters," Journal of Development Economics, Elsevier, vol. 88(2), pages 221-231, March.
    4. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    5. 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.
    6. Miaw-Chwen Lee & Andrew M. Jones, 2004. "How did Dentists Respond to the Introduction of Global Budgets in Taiwan? An Evaluation Using Individual Panel Data," International Journal of Health Economics and Management, Springer, vol. 4(4), pages 307-326, December.
    7. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dreher, Axel & Voigt, Stefan, 2011. "Does membership in international organizations increase governments' credibility? Testing the effects of delegating powers," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 326-348, September.
    2. Krishna Chaitanya Vadlamannati & Artur Tamazian, 2017. "Are Left-Wing Governments Really Pro-Labor? An Empirical Investigation for Latin America," Kyklos, Wiley Blackwell, vol. 70(1), pages 129-160, February.
    3. Niklas Potrafke, 2012. "Political cycles and economic performance in OECD countries: empirical evidence from 1951–2006," Public Choice, Springer, vol. 150(1), pages 155-179, January.
    4. Dreher, Axel & Mikosch, Heiner & Voigt, Stefan, 2015. "Membership has its Privileges – The Effect of Membership in International Organizations on FDI," World Development, Elsevier, vol. 66(C), pages 346-358.
    5. Emmanuel Owusu-Sekyere & Reneé van Eyden & Francis M Kemegue, 2014. "Remittances and the Dutch Disease in Sub-Saharan Africa: A Dynamic Panel Approach," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(3), September.
    6. Haq, Mamiza & Faff, Robert & Seth, Rama & Mohanty, Sunil, 2014. "Disciplinary tools and bank risk exposure," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 37-64.
    7. Hammermann, Felix & Flanagan, Mark, 2007. "What Explains Persistent Inflation Differentials Across Transition Economies?," Kiel Working Papers 1373, Kiel Institute for the World Economy (IfW Kiel).
    8. Haq, Mamiza & Heaney, Richard, 2012. "Factors determining European bank risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 696-718.
    9. Caixe, Daniel Ferreira, 2022. "Corporate governance and investment sensitivity to policy uncertainty in Brazil," Emerging Markets Review, Elsevier, vol. 51(PB).
    10. Merkert, Rico & Swidan, Hassan, 2019. "Flying with(out) a safety net: Financial hedging in the airline industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 206-219.
    11. Barabas, György & Kitlinski, Tobias & Schmidt, Christoph M. & Schmidt, Torsten & Siemers, Lars-H. & Brilon, Werner, 2010. "Verkehrsinfrastrukturinvestitionen: Wachstumsaspekte im Rahmen einer gestaltenden Finanzpolitik. Endbericht - Januar 2010. Forschungsprojekt im Auftrag des Bundesministeriums der Finanzen. Projektnumm," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 72601.
    12. Potrafke, Niklas, 2010. "The growth of public health expenditures in OECD countries: Do government ideology and electoral motives matter?," Journal of Health Economics, Elsevier, vol. 29(6), pages 797-810, December.
    13. Robert A Baade & Robert Baumann & Victor A Matheson, 2009. "Rejecting “Conventional” Wisdom: Estimating the Economic Impact of National Political Conventions," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 35(4), pages 520-530.
    14. Hammermann, Felix, 2007. "Nonmonetary Determinants of Inflation in Romania: A Decomposition," Kiel Working Papers 1322, Kiel Institute for the World Economy (IfW Kiel).
    15. Thomas K.J. McDermott & Frank Barry & Richard S.J. Tol, 2014. "Disasters and development: natural disasters, credit constraints, and economic growth," Oxford Economic Papers, Oxford University Press, vol. 66(3), pages 750-773.
    16. Klomp, Jeroen & Hoogezand, Barry, 2018. "Natural disasters and agricultural protection: A panel data analysis," World Development, Elsevier, vol. 104(C), pages 404-417.
    17. W. Robert Reed & Haichun Ye, 2007. "A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present," Working Papers in Economics 07/01, University of Canterbury, Department of Economics and Finance.
    18. Isabel‐María García‐Sánchez & Jennifer Martínez‐Ferrero, 2018. "How do Independent Directors Behave with Respect to Sustainability Disclosure?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 25(4), pages 609-627, July.
    19. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    20. Yakovlev, Pavel & Tosun, Mehmet S. & Lewis, William P., 2012. "Legislative Term Limits and State Aid to Local Governments," IZA Discussion Papers 6456, Institute of Labor Economics (IZA).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cbt:econwp:10/53. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Albert Yee (email available below). General contact details of provider: https://edirc.repec.org/data/decannz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.