Random Coefficient Models for Time-Series-Cross-Section Data: Monte Carlo Experiments
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.
Additional Information© 2006 The Author. Published by Oxford University Press on behalf of the Society for Political Methodology. Advance Access publication June 27, 2006. 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. Originally submitted as Social Science Working Paper 1205 (Sept. 2004).
Published - beck+katz07.pdf