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A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Published online by Cambridge University Press:  04 January 2017

Boris Shor ,
Joseph Bafumi ,
Luke Keele  and
David Park
Boris Shor
Affiliation:
Harris School of Public Policy Studies, University of Chicago, 1155 E. 60th Street, Suite 185, Chicago, IL 60637. e-mail: bshor@uchicago.edu (corresponding author)
Joseph Bafumi
Affiliation:
Department of Government, Dartmouth College,6108 Silsby HallHanover, NH 03755. e-mail: joseph.bafumi@dartmouth.edu
Luke Keele
Affiliation:
Department of Political Science, Ohio State University,2137 Derby Hall, 154 N Oval Mall, Columbus, OH 43210. e-mail: keele.4@polisci.osu.edu
David Park
Affiliation:
Department of Political Science, George Washington University,1922 F Street, N.W. 414C, Washington, DC 20052. e-mail: dkp@gwu.edu
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Abstract

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.

Type
Research Article
Information
Political Analysis , Volume 15 , Issue 2: Special Issue: Time-Series Cross-Sectional Analysis , Spring 2007 , pp. 165 - 181
Copyright
Copyright © The Author 2007. Published by Oxford University Press on behalf of the Society for Political Methodology 

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