A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present
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.
|Date of creation:||30 Apr 2007|
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- 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.
- David Roodman, 2006.
"How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata,"
103, Center for Global Development.
- 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.
- 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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