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Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach

Guohua Feng (), Chuan Wang () and Xibin Zhang ()
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Guohua Feng: University of North Texas
Chuan Wang: Zhongnan University of Economics and Law
Xibin Zhang: Monash University

Journal of Productivity Analysis, 2019, vol. 51, issue 1, 1-19

Abstract: Abstract We propose a kernel-based Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The primary feature of this framework is that the unknown distribution of inefficiency is approximated by a transformed Rosenblatt-Parzen kernel density estimator. To justify the kernel-based model, we conduct a Monte Carlo study and also apply the model to a panel of U.S. large banks. Simulation results show that the kernel-based model is capable of providing more precise estimation and prediction results than the commonly-used exponential stochastic frontier model. The Bayes factor also favors the kernel-based model over the exponential model in the empirical application.

Keywords: Kernel density estimation; Efficiency measurement; Stochastic distance frontier; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 D24 G21 (search for similar items in EconPapers)
Date: 2019
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