Pseudolikelihood estimation of the stochastic frontier model

Abstract

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood estimation. An alternative approach, pseudolikelihood estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in several advanced settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross sectional framework. We seek to produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that pseudolikelihood estimation enjoys almost identical performance to maximum likelihood estimation across a range of scenarios, and out performs maximum likelihood estimation in settings where the distribution of inefficiency is incorrectly specified.