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Activity Number: 657 - Bayesian and Empirical Bayes
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #304736 Presentation
Title: Hierarchical Bayesian Link Model for Stochastic Frontier Production Function Model
Author(s): Seongho Song* and Younshik Chung and David T. Yi
Companies: University of Cincinnati and Pusan National University and Xavier University
Keywords: Bayesian Link Model; Stochastic Frontier Production Function Model; Technical Efficency; Markov Chain Monte Carlo

A stochastic frontier production function (SFPF) has been considered in the case that there is the effect of non-negative technical inefficiency in the data. In the literature, many researchers have discussed how to determine the explanatory variables which affect the technical inefficiency effects in the SFPF. In this research, we consider a hierarchical Bayesian link model for the SFPF to investigate the hierarchically structured data. It turns out that the proposed model naturally gives us the dependent covariance structure within a sub-group and the independence between sub-groups. We consider Bayesian analysis to estimate the model parameters in the model as well as the inefficiency model using Markov Chain Monte Carlo (MCMC).

Authors who are presenting talks have a * after their name.

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