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

17 Pages Posted: 12 Aug 2009 Last revised: 15 Jul 2013

See all articles by Boris Shor

Boris Shor

University of Houston - Department of Political Science

Joseph Bafumi

Dartmouth College - Department of Government

Luke Keele

Pennsylvania State University

David Park

George Washington University

Date Written: 2007

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.

Keywords: methodology, TSCS data

Suggested Citation

Shor, Boris and Bafumi, Joseph and Keele, Luke and Park, David, A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data (2007). Political Analysis, Vol. 15, Issue 2, pp. 165-181, 2007, Available at SSRN: https://ssrn.com/abstract=1447746 or http://dx.doi.org/10.1093/pan/mpm006

Boris Shor (Contact Author)

University of Houston - Department of Political Science ( email )

Houston, TX 77204-3011
United States

Joseph Bafumi

Dartmouth College - Department of Government ( email )

Hanover, NH
United States

Luke Keele

Pennsylvania State University ( email )

Harrisburg, PA
United States

David Park

George Washington University ( email )

2121 I Street NW
Washington, DC 20052
United States

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