Estimation of simultaneous equation models with latent dependent variables: a Monte Carlo evaluation

Abstract

Three methods can be used to estimate simultaneous equation models with latent dependent variables: two-stage, minimum distance (MD) and full-information maximum likelihood. Theoretically all the three methods provide asymptotically consistent estimates, but the performance of these estimators in finite samples cannot be determined in theory. This letter evaluates the performance of these estimators in finite samples using Monte Carlo simulation. The results show that the MD estimator performs very poorly; overall the full information maximum likelihood estimator performs better than the other two estimators.