An introduction to efficiency measurement using Bayesian stochastic frontier models

2001 ◽  
Vol 3 (2) ◽  
pp. 287 ◽  
Author(s):  
Efthymios G. Tsionas
2011 ◽  
Vol 2011 ◽  
pp. 1-25 ◽  
Author(s):  
Subal C. Kumbhakar ◽  
Efthymios G. Tsionas

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.


Author(s):  
Caroline Khan ◽  
Mike G. Tsionas

AbstractIn this paper, we propose the use of stochastic frontier models to impose theoretical regularity constraints (like monotonicity and concavity) on flexible functional forms. These constraints take the form of inequalities involving the data and the parameters of the model. We address a major concern when statistically endogenous variables are present in these inequalities. We present results with and without endogeneity in the inequality constraints. In the system case (e.g., cost-share equations) or more generally, in production function-first-order conditions case, we detect an econometric problem which we solve successfully. We provide an empirical application to US electric power generation plants during 1986–1997, previously used by several authors.


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