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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.

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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/0701.pdf
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Bibliographic Info

Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 07/01.

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Length: 49 pages
Date of creation: 30 Apr 2007
Date of revision:
Handle: RePEc:cbt:econwp:07/01

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Keywords: Panel Data estimation; Monte Carlo analysis; FGLS; PCSE; Groupwise Heteroscedasticity; Serial Correlation; Cross-sectional Dependence; Stata; EViews;

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  1. 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.
  2. 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.
  3. Grubb, David & Magee, Lonnie, 1988. "A Variance Comparison of OLS and Feasible GLS Estimators," Econometric Theory, Cambridge University Press, vol. 4(02), pages 329-335, August.
  4. David Roodman, 2006. "How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata," Working Papers 103, Center for Global Development.
  5. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
  6. Rafael E. De Hoyos & Vasilis Sarafidis, 2006. "Testing for cross-sectional dependence in panel-data models," Stata Journal, StataCorp LP, vol. 6(4), pages 482-496, December.
  7. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
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Cited by:
  1. 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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