Unknown latent structure and inefficiency in panel stochastic frontier models

Abstract

This paper extends the fixed effect panel stochastic frontier models to allow group heterogeneity in the slope coefficients. We propose the first-difference penalized maximum likelihood (FDPML) and control function penalized maximum likelihood (CFPML) methods for classification and estimation of latent group structures in the frontier as well as inefficiency. Monte Carlo simulations show that the proposed approach performs well in finite samples. An empirical application is presented to show the advantages of data-determined identification of the heterogeneous group structures in practice.