scholarly journals Efficiency Measurement Using a “True” Random Effects and Random Parameter Stochastic Frontier Models: An Application to Rural and Community Banks in Ghana

2013 ◽  
Vol 04 (12) ◽  
pp. 864-870 ◽  
Author(s):  
Michael Danquah ◽  
Alfred Barimah ◽  
Williams Ohemeng
Keyword(s):  
Random Effects ◽  
Stochastic Frontier ◽  
Random Parameter ◽  
Efficiency Measurement ◽  
Community Banks ◽  
Stochastic Frontier Models

scholarly journals Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Grigorios Emvalomatis ◽  
Spiro E. Stefanou ◽  
Alfons Oude Lansink
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Fixed Effects ◽  
Likelihood Function ◽  
Stochastic Frontier ◽  
General Context ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Incidental Parameters ◽  
Stochastic Frontier Models

Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific constant terms with the explanatory variables. Monte Carlo experiments are performed in the specific context of stochastic frontier models to examine and compare the sampling properties of the proposed estimator with those of the random-effects and correlated random-effects estimators. The results suggest that the estimator is unbiased even in short panels. An application to a cross-country panel of EU manufacturing industries is presented as well. The proposed estimator produces a distribution of efficiency scores suggesting that these industries are highly efficient, while the other estimators suggest much poorer performance.

scholarly journals Some Recent Developments in Efficiency Measurement in Stochastic Frontier Models

2011 ◽  
Vol 2011 ◽  
pp. 1-25 ◽  
Author(s):  
Subal C. Kumbhakar ◽  
Efthymios G. Tsionas
Keyword(s):  
Latent Class ◽  
Stochastic Frontier ◽  
Local Maximum ◽  
Latent Class Models ◽  
Economic Behavior ◽  
Efficiency Measurement ◽  
Likelihood Method ◽  
Stochastic Frontier Models ◽  
Recent Developments ◽  
Local Maximum Likelihood

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.

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.

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