Political Analysis Advance Access originally published online on March 7, 2007
Political Analysis 2007 15(2):165-181; doi:10.1093/pan/mpm006
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A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data
Harris School of Public Policy Studies, University of Chicago,1155 E. 60th Street, Suite 185, Chicago, IL 60637
Department of Government, Dartmouth College,6108 Silsby HallHanover, NH 03755 e-mail: joseph.bafumi{at}dartmouth.edu
Department of Political Science, Ohio State University,2137 Derby Hall, 154 N Oval Mall, Columbus, OH 43210 e-mail: keele.4{at}polisci.osu.edu
Department of Political Science, George Washington University,1922 F Street, N.W. 414C, Washington, DC 20052 e-mail: dkp{at}gwu.edu
e-mail: bshor{at}uchicago.edu (corresponding author)
The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.
Authors' note: A previous version of this article was presented at the 2005 Midwest Political Science Meeting. We would like to thank the following for comments and advice in writing this paper: Andrew Gelman, Nathaniel Beck, Greg Wawro, Sam Cooke, John Londregan, David Brandt. Any errors are our own.