@article{be7287f287504f5d950faa73bdbf8304,
title = "Random coefficient models for time-series-cross-section data: Monte Carlo experiments",
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.",
author = "Nathaniel Beck and Katz, {Jonathan N.}",
note = "Funding Information: Authors{\textquoteright} 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. 1We use the term comparative political economy instead of comparative politics since there have recently been a series of applications in comparative political behavior that use methods similar to those investigated here. Those applications are, as we shall see, to data sets that do not look like typical TSCS data sets, and so we wish to keep the two types of applications distinct.",
year = "2007",
month = mar,
doi = "10.1093/pan/mpl001",
language = "English (US)",
volume = "15",
pages = "182--195",
journal = "Political Analysis",
issn = "1047-1987",
publisher = "Cambridge University Press",
number = "2",
}