Random Coefficient Models for Time-Series–Cross-Section Data: Monte Carlo Experiments of Finite Sample Properties∗

This article considers random coefficient models (RCMs) for time-series–crosssection data. These models allow for unit to unit variation in the model parameters. The heart of the paper 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 FGLS estimator of the RCM models in always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.