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Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments

Published online by Cambridge University Press:  04 January 2017

Nathaniel Beck  and
Jonathan N. Katz
Nathaniel Beck
Affiliation:
Department of Politics, New York University, New York, NY 10003. e-mail: nathaniel.beck@nyu.edu (corresponding author)
Jonathan N. Katz
Affiliation:
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125. e-mail: jkatz@caltech.edu
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Abstract

This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

Type
Research Article
Information
Political Analysis , Volume 15 , Issue 2: Special Issue: Time-Series Cross-Sectional Analysis , Spring 2007 , pp. 182 - 195
Copyright
Copyright © The Author 2006. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Adolph, Christopher, Butler, Daniel M., and Wilson, Sven E. 2005. Which time-series cross-section estimator should I use now? Guidance from Monte Carlo experiments. Paper presented at the 2005 annual meeting of the American Political Science Association, Washington, DC.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. 2004. Random coefficient models for time-series-cross-section data. Social Science Working Paper 1205, Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, September, 2004.Google Scholar
Efron, Bradley, and Morris, Carl. 1975. Data analysis using Stein's estimator and its generalizations. Journal of the American Statistical Association 70: 311–9.Google Scholar
Garrett, Geoffrey. 1998. Partisan politics in the global economy. New York: Cambridge University Press.CrossRefGoogle Scholar
Greene, William. 2003. Econometric analysis. 5th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hsiao, Cheng. 2003. Analysis of panel data. 2nd ed. New York: Cambridge University Press.Google Scholar
Kedar, Orit, and Shively, Phillip. 2005. Introduction to the special issue. Political Analysis 13: 1–4.Google Scholar
Pinheiro, José C., and Bates, Douglas M. 2000. Mixed effects models in S and S-Plus. New York: Springer.Google Scholar
Steenbergen, Marco R., and Jones, Bradford S. 2002. Modeling multilevel data structures. American Journal of Political Science 46: 218–37.CrossRefGoogle Scholar
Swamy, P. A. V. B. 1971. Statistical inference in random coefficient models. New York: Springer-Verlag.CrossRefGoogle Scholar
Wallerstein, Michael, and Western, Bruce. 2000. Unions in decline? What has changed and why. Annual Review of Political Science 3: 355–77.Google Scholar
Western, Bruce. 1997. Between class and market: Postwar unionization in capitalist democracies. Princeton, NJ: Princeton University Press.Google Scholar
Western, Bruce. 1998. Causal heterogeneity in comparative research: A Bayesian hierarchical modelling approach. American Journal of Political Science 42: 1233–59.CrossRefGoogle Scholar