Pseudolikelihood estimation of the stochastic frontier model
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, decouples estimation of the error component structure and the production frontier, has been adopted in both the nonparametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross sectional framework. We 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 performance metrics, and for certain metrics outperforms maximum likelihood estimation when the distribution of inefficiency is incorrectly specied.