ESTIMATION AND TESTING IN THE RANDOM EFFECTS PROBIT MODEL

This paper examines the finite-sample properties of the random effects probit estimator in comparison to the standard probit estimator and t he standard probit estimator with a corrected asymptotic covariance ma trix. The Monte Carlo experiment considers data-generating processes c onsistent with longitudinal data and also data from sample surveys. Th e probit estimator with corrected asymptotic covariance matrix works s urprisingly well over a wide range of parametric configurations and is recommended as long as an estimate of the error correlation is not of high importance.