Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T12:28:39.507Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arellano, Manuel. 1987. Computing robust standard errors for within-group estimators. Oxford Bulletin of Economics and Statistics 49: 431–4.Google Scholar
Bafumi, Joseph. 2004a. The macro micro link in vote choice models: A Bayesian multilevel approach. Presented at the 2004 annual meeting of the Midwest Political Science Association, Chicago, IL.Google Scholar
Bafumi, Joseph. 2004b. The stubborn American voter. Presented at the 2004 annual meeting of the American Political Science Association, Chicago, IL.Google Scholar
Bafumi, Joseph, and Gelman, Andrew. 2006. Fitting multilevel models when predictors and group effects correlate. Paper presented at the annual meeting of the American Political Science Association, Philadelphia, PA.Google Scholar
Bafumi, Joseph, Gelman, Andrew, Park, David K., and Kaplan, Noah. 2005. Practical issues in implementing and understanding Bayesian ideal point estimation. Political Analysis 13: 171–87.Google Scholar
Bartels, Larry M. 1996. Pooling disparate observations. American Journal of Political Science 40: 905–42.Google Scholar
Beck, Nathaniel. 2001. Time-series-cross-section data: What have we learned in the past few years. Annual Review of Political Science 4: 271–93.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N. 1995. What to do (and not to do) with time-series cross-section data. American Political Science Review 89: 634–47.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N. 2007. Random coefficient models for time-series-cross-section data: Monte Carlo experiments. Political Analysis. 10.1093/pan/mpl001.Google Scholar
Browne, William J., and Draper, David. 2006. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis 1: 473–514.Google Scholar
Gelman, Andrew. 2006. Multilevel (hierarchical) modeling: What it can and can't do. Technometrics 48: 432–5.Google Scholar
Gelman, Andrew, Carlin, John S., Stern, Hal S., and Rubin, Donald B. 2003. Bayesian data analysis. 2nd ed. Boca Raton, FL: Chapman and Hall.Google Scholar
Gelman, Andrew, and Hill, Jennifer. 2006. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.Google Scholar
Gelman, Andrew, and King, Gary. 1993. Why are American presidential election campaign polls so variable when votes are so predictable? British Journal of Political Science 23: 409–51.Google Scholar
Gelman, Andrew, Shor, Boris, Bafumi, Joseph, and Park, David. Forthcoming. Rich state, poor state, red state, blue state: What's the matter with Connecticut? Quarterly Journal of Political Science.Google Scholar
Gill, Jeff, and Walker, Lee D. 2005. Elicited priors for Bayesian model specifications in political science research. The Journal of Politics 67: 841–72.Google Scholar
Jackman, Simon. 2000. Estimation and inference are missing data problems: Unifying social science statistics via Bayesian simulation. Political Analysis 8: 307–32.Google Scholar
Jackman, Simon. 2004. Bayesian analysis for political research. Annual Review of Political Science 7: 483–505.Google Scholar
Kristensen, Ida P., and Wawro, Gregory. 2003. Lagging the dog? The robustness of panel corrected standard errors in the presence of serial correlation and observation specific effects. Presented at the annual meeting of the Political Methodology Conference, Minneapolis, MN.Google Scholar
Park, David. 2001. Representation in the American states: The 100th Senate and their electorate. Working paper.Google Scholar
Park, David K., Gelman, Andrew, and Bafumi, Joseph. 2004. Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Political Analysis 12: 375–85.Google Scholar
Raudenbush, Stephen W., and Bryk, Anthony S. 2002. Hierarchical linear models: Applications and data analysis methods. 2nd ed. Newbury Park, CA: Sage Publications.Google Scholar
Shor, Boris. 2004. Taking context into account: Testing partisan, institutional, and electoral theories of political influence on defense procurement in congressional districts, 98th-102nd congress. Paper presented at the annual meeting of the American Political Science Association, Chicago, IL.Google Scholar
Shor, Boris. 2006. A Bayesian multilevel model of federal spending, 1983-2001. Working paper.Google Scholar
Shor, Boris, Bafumi, Joseph, Park, David K., and Gelman, Andrew. 2003. Multilevel modeling of time series data. Paper presented at the annual meeting of the American Political Science Association, Philadelphia, PA.Google Scholar
Steenbergen, Marco R., and Jones, Bradford S. 2002. Modeling multilevel data structures. American Journal of Political Science 46: 218–37.Google Scholar
Stimson, James A. 1985. Regression models in space and time: A statistical essay. American Journal of Political Science 29: 914–47.Google Scholar
Western, Bruce. 1998. Causal heterogeneity in comparative research: A Bayesian modelling approach. American Journal of Political Science 42: 1233–59.Google Scholar
Western, Bruce, and Jackman, Simon. 1994. Bayesian inference for comparative research. American Political Science Review 88: 412–33.Google Scholar