Random coefficient models for time-series-cross-section data: Monte Carlo experiments

Nathaniel Beck, Jonathan N. Katz

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)182-195
Number of pages14
JournalPolitical Analysis
Volume15
Issue number2
DOIs
StatePublished - Mar 2007

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations

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