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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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