Semiparametric maximum likelihood estimation of stochastic frontier model with errors-in-variables

This paper presents a new technique to analyze a stochastic frontier model when covariates are incorporated with measurement errors. We propose a semiparametric mixture likelihood method to estimate the stochastic frontier model which is free from any erroneous specification of the distribution of latent covariates. Some numerical studies including a real data analysis were done, which highly support the proposed approach.