Reconsidering heterogeneity in panel data estimators of the stochastic frontier model

This paper examines several extensions of the stochastic frontier that account for unmeasured heterogeneity as well as firm inefficiency. The fixed effects model is extended to the stochastic frontier model using results that specifically employ its nonlinear specification. Based on Monte Carlo results, we find that the incidental parameters problem operates on the coefficient estimates in the fixed effects stochastic frontier model in ways that are somewhat at odds with other familiar results. We consider a special case of the random parameters model that produces a random effects model that preserves the central feature of the stochastic frontier model and accommodates heterogeneity. We then examine random parameters and latent class models. In these cases, explicit models for firm heterogeneity are built into the stochastic frontier. Comparisons with received results for these models are presented in an application to the U.S. banking industry.