Stochastic Frontier Models for Discrete Output Variables

2019 ◽  
pp. 275-300
Author(s):  
Eduardo Fé
Keyword(s):  
Stochastic Frontier ◽  
Stochastic Frontier Models

scholarly journals Constraints in models of production and cost via slack-based measures

2021 ◽  
Author(s):  
Caroline Khan ◽  
Mike G. Tsionas
Keyword(s):  
Power Generation ◽  
Stochastic Frontier ◽  
Inequality Constraints ◽  
Order Conditions ◽  
Flexible Functional Forms ◽  
Cost Share ◽  
First Order ◽  
Stochastic Frontier Models ◽  
Endogenous Variables ◽  
Functional Forms

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.

scholarly journals Measurement of technical efficiency in the case of heterogeneity of technologies used between firms – Based on evidence from Polish crop farms

2021 ◽  
Author(s):  
Jerzy Marzec ◽  
Andrzej Pisulewski
Keyword(s):  
Technical Efficiency ◽  
Production Function ◽  
Prior Distribution ◽  
Prior Information ◽  
Stochastic Frontier ◽  
Returns To Scale ◽  
Random Coefficients ◽  
Point Of View ◽  
Model Specification ◽  
Stochastic Frontier Models

In the present study, we have investigated several competing stochastic frontier models which differ in terms of the form of the production function (Cobb-Douglas or translog), inefficiency distribution (half-normal or exponential distribution) and type of prior distribution for the parameters (hierarchical or non-hierarchical from the Bayesian point of view). This last distinction corresponds to a difference between random coefficients and fixed coefficients models. Consequently, this study aims to examine to what extent inferences about estimates of farms' efficiency depend on the above assumptions. Moreover, the study intends to investigate how far the production function's characteristics are affected by the choice of the type of prior distribution for the parameters. First of all, it was found that the form of the production function does not impact the efficiency scores. Secondly, we found that measures of technical efficiency are sensitive to distributional assumptions about the inefficiency term. Finally, we have revealed that estimates of technical efficiency are reasonably robust to the prior information about the parameters of crop farms' production technology. There is also a resemblance in the elasticity of output with respect to inputs between the models considered in this paper. Additionally, the measurement of returns to scale is not sensitive to model specification.

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