Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach |
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Authors: | Feng Guohua Wang Chuan Zhang Xibin |
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Affiliation: | 1.Department of Economics, University of North Texas, Denton, TX, 76203, USA ;2.Wenlan School of Business, Zhongnan University of Economics and Law, 430073, Wuhan, Hubei, China ;3.Department of Econometrics and Business Statistics, Monash University, Caulfield East, Victoria, 3145, Australia ; |
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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. |
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