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Political Analysis Advance Access first published online on June 27, 2006
This version published online on July 7, 2006
Political Analysis, doi:10.1093/pan/mpl001
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Article

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

Nathaniel Beck 1 * and Jonathan N. Katz 2

1 Department of Politics, New York University, New York, NY 10003
2 Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125

* To whom correspondence should be addressed.
Nathaniel Beck, E-mail: nathaniel.beck{at}nyu.edu


  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.

Equation 6 and Footnote 3 have been corrected.
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