Estimation of a dynamic stochastic frontier model using likelihood‐based approaches

Summary This paper considers a panel stochastic production frontier model that allows the dynamic adjustment of technical inefficiency. In particular, we assume that inefficiency follows an AR(1) process. That is, the current year's inefficiency for a firm depends on its past inefficiency plus a transient inefficiency incurred in the current year. Interfirm variations in the transient inefficiency are explained by some firm‐specific covariates. We consider four likelihood‐based approaches to estimate the model: the full maximum likelihood, pairwise composite likelihood, marginal composite likelihood, and quasi‐maximum likelihood approaches. Moreover, we provide Monte Carlo simulation results to examine and compare the finite‐sample performances of the four above‐mentioned likelihood‐based estimators of the parameters. Finally, we provide an empirical application of a panel of 73 Finnish electricity distribution companies observed during 2008–2014 to illustrate the working of our proposed models.