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A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present

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Abstract

This study employs Monte Carlo experiments to evaluate the performances of a number of common panel data estimators when serial correlation and cross-sectional dependence are both present. It focuses on fixed effects models with less than 100 cross-sectional units and between 10 and 25 time periods (such as are commonly employed in empirical growth studies). Estimator performance is compared on two dimensions: (i) root mean square error and (ii) accuracy of estimated confidence intervals. An innovation of our study is that our simulated panel data sets are designed to look like “real-world” panel data. We find large differences in the performances of the respective estimators. Further, estimators that perform well on efficiency grounds may perform poorly when estimating confidence intervals, and vice versa. Our experimental results form the basis for a set of estimator recommendations. These are applied to “out of sample” simulated panel data sets and found to perform well.

Suggested Citation

  • 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.
    • Handle: RePEc:cbt:econwp:07/01
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    File URL: https://repec.canterbury.ac.nz/cbt/econwp/0701.pdf
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    1. Grubb, David & Magee, Lonnie, 1988. "A Variance Comparison of OLS and Feasible GLS Estimators," Econometric Theory, Cambridge University Press, vol. 4(2), pages 329-335, August.
    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.
    3. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    4. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LLC, vol. 9(1), pages 86-136, March.
    5. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
    6. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    7. Rafael E. De Hoyos & Vasilis Sarafidis, 2006. "Testing for cross-sectional dependence in panel-data models," Stata Journal, StataCorp LLC, vol. 6(4), pages 482-496, December.
    8. Michael Wasylenko, 1997. "Taxation and economic development: the state of the economic literature," New England Economic Review, Federal Reserve Bank of Boston, issue Mar, pages 37-52.
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    Cited by:

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    2. Preston, John & Almutairi, Talal, 2013. "Evaluating the long term impacts of transport policy: An initial assessment of bus deregulation," Research in Transportation Economics, Elsevier, vol. 39(1), pages 208-214.

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    More about this item

    Keywords

    Panel Data estimation; Monte Carlo analysis; FGLS; PCSE; Groupwise Heteroscedasticity; Serial Correlation; Cross-sectional Dependence; Stata; EViews;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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