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Another Look At What To Do With Time-Series Cross-Section Data

Author

Listed:
  • Xiujian Chen

    (University of Oklahoma)

  • Shu Lin

    (University of Oklahoma)

  • W. Robert Reed

    (University of Oklahoma)

Abstract
Our study revisits Beck and Katz’ (1995) comparison of the Parks and PCSE estimators using time-series, cross-sectional data (TSCS). Our innovation is that we construct simulated statistical environments that are designed to closely match “real-world,” TSCS data. We pattern our statistical environments after income and tax data on U.S. states from 1960-1999. While PCSE generally does a better job than Parks in estimating standard errors, it too can be unreliable, sometimes producing standard errors that are substantially off the mark. Further, we find that the benefits of PCSE can come at a substantial cost in estimator efficiency. Based on our study, we would give the following advice to researchers using TSCS data: Given a choice between Parks and PCSE, we recommend that researchers use PCSE for hypothesis testing, and Parks if their primary interest is accurate coefficient estimates.

Suggested Citation

  • Xiujian Chen & Shu Lin & W. Robert Reed, 2005. "Another Look At What To Do With Time-Series Cross-Section Data," Econometrics 0506004, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpem:0506004
    Note: Type of Document - pdf; pages: 26
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    References listed on IDEAS

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    Cited by:

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    2. Mellati, Ali, 2008. "Uncertainty and investment in private sector: An analytical argument and a review of the economy of Iran," MPRA Paper 26655, University Library of Munich, Germany.
    3. Piotr Ciżkowicz & Andrzej Rzońca & Wiktor Wojciechowski, 2012. "Determinanty regionalnych różnic w dynamice liczby pracujących w Polsce w latach 1999-2008," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 11-12, pages 59-77.
    4. Eric C. Y. Ng & Malick Souare, 2014. "On investment and exchange-rate movements," Applied Economics, Taylor & Francis Journals, vol. 46(19), pages 2301-2315, July.
    5. Saravanakumar, V. & Balasubramanian, 2018. "Assessment of climate change impacts and adaptation strategies on Sorghum production in Tamil Nadu, India," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276012, International Association of Agricultural Economists.
    6. V. Saravanakumar, 2015. "Impact of Climate Change on Yield of Major Food Crops in Tamil Nadu, India," Working Papers id:7555, eSocialSciences.
    7. Aleksander Aristovnik, 2006. "Current Account Reversals and Persistency in Transition Regions," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 9(1), pages 1-43, May.
    8. Aleksander Aristovnik & Stanka Setnikar-Cankar, 2006. "How Excessive are External Imbalances in Selected Transition Countries?," Prague Economic Papers, Prague University of Economics and Business, vol. 2006(3), pages 243-267.
    9. Kunsoo Han & Robert J. Kauffman & Barrie R. Nault, 2011. "Research Note ---Returns to Information Technology Outsourcing," Information Systems Research, INFORMS, vol. 22(4), pages 824-840, December.
    10. Étienne Bordeleau & Allan Crawford & Christopher Graham, 2009. "Regulatory Constraints on Bank Leverage: Issues and Lessons from the Canadian Experience," Discussion Papers 09-15, Bank of Canada.
    11. Özdemir Onur, 2019. "Financialization and the Labor Share of Income," Review of Economic Perspectives, Sciendo, vol. 19(4), pages 265-306, December.
    12. Pitelis, Christos & Vasilaros, Vassilis, 2009. "The Determinants of Value Creation at the Firm, Industry and National Levels: A Framework and Evidence," Papers DYNREG37, Economic and Social Research Institute (ESRI).
    13. Kadri Männasoo, 2012. "Determinants of bank interest spread in Estonia," Bank of Estonia Working Papers wp2012-1, Bank of Estonia, revised 22 Feb 2012.
    14. Fernando Martín-Mayoral & Juan Fernández Sastre, 2017. "Determinants of social spending in Latin America during and after the Washington consensus: a dynamic panel error-correction model analysis," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-32, December.
    15. 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.
    16. Aleksander Aristovnik, 2005. "Current Account Reversals In Selected Transition Countries," International Finance 0510021, University Library of Munich, Germany.

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    More about this item

    Keywords

    Panel Data; Panel Corrected Standard Errors; Monte Carlo analysis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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