Political Analysis Advance Access originally published online on June 27, 2006
Political Analysis 2007 15(2):182-195; doi:10.1093/pan/mpl001
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Random Coefficient Models for Time-Series–Cross-Section Data: Monte Carlo Experiments
Department of Politics, New York University, New York, NY 10003
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125
e-mail: jkatz{at}caltech.edu
e-mail: nathaniel.beck{at}nyu.edu (corresponding author)
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.
Authors' note: We gratefully acknowledge the financial support of the National Science Foundation. Katz also acknowledges the support of the Center for Advanced Study in the Behavioral Sciences. We are thankful to Jake Bowers, Rob Franzese, Andy Gelman, Sandy Gordon, Bill Greene, and Luke Keele for comments; to Larry Bartels for always reminding us that our judgment may outperform the data; as well as to the anonymous reviewers of Political Analysis.
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