Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach

The productive efficiency of a firm can be seen as composed of two parts, one persistent and one transient. The received empirical literature on the measurement of productive efficiency has paid relatively little attention to the difference between these two components. Ahn, Good and Sickles (2000) suggested some approaches that pointed in this direction. The possibility was also raised in Greene (2004), who expressed some pessimism over the possibility of distinguishing the two empirically. Recently, Colombi et al. (2010), and Kumbhakar and Tsionas (2012), in a milestone extension of the stochastic frontier methodology have proposed a tractable model based on panel data the promises to provide separate estimates of the two components of efficiency. The approach developed in the original presentation proved very cumbersome actually to implement in practice. The latter study also suggested a partial solution, but stopped short of a full practical implementation of the original model. (The sensitivity of the Bayesian approach to the need to have informative priors over the efficiency distribution, which is the main object of estimation, also remains to be settled.) Finally, Kumbhakar, Lien and Hardaker (2012), in their survey of panel data models, suggested a method of moments estimator for the LR/SR model based on simple OLS. In this paper, we develop a practical full information maximum simulated likelihood estimator for the model. The approach is very effective and strikingly simple to apply, and uses all of the sample sistributional information to obtain the estimates. We alsoimplement the panel data counterpart of the JLMS (1982) estimator for technical or cost inefficiency. The technique is applied in a study of the cost efficiency of Swiss railways.