Abstract
This paper addresses some of the recent developments in efficiency measurement using stochastic frontier (SF) models in some selected areas. The following three issues are discussed in details. First, estimation of SF models with input‐oriented technical efficiency. Second, estimation of latent class models to address technological heterogeneity as well as heterogeneity in economic behavior. Finally, estimation of SF models using local maximum likelihood method. Estimation of some of these models in the past was considered to be too difficult. We focus on the advances that have been made in recent years to estimate some of these so‐called difficult models. We complement these with some developments in other areas as well.
Highlights
In this paper we focus on three issues
In a primal setup two measures of technical efficiency are mostly used in the efficiency literature
In the OO model the elasticities and returns to scale will be independent of the technical inefficiency because technical efficiency i.e., assumed to be independent of inputs enters multiplicatively into the production function
Summary
We discuss issues mostly econometric related to input-oriented IO and output-oriented OO measures of technical inefficiency and talk about the estimation of production functions with IO technical inefficiency. We consider profit- revenue- maximizing and cost-minimizing behaviors with technical inefficiency. In our mixing/latent class model, first we consider a system approach in which some producers maximize profit while others minimize cost, and we use a distance function approach, and mix the input and output distance functions in which it is assumed, at least implicitly, that some producers maximize revenue while others minimize cost. The prior probability in favor of profit revenue maximizing behavior is assumed to depend on some exogenous variables. Journal of Probability and Statistics method to address the flexibility issue functional form, heteroskedasticity, and determinants of technical inefficiency
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