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Activity Number: 421 - Advances in Bayesian Modeling and Inferential Methods
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #329283 Presentation
Title: Hierarchical Bayesian Analysis for Stochastic Frontier Production Function Model
Author(s): Seongho Song* and Younshik Chung and David Taesok Yi
Companies: University of Cincinnati and Pusan National University and Xavier University
Keywords: Bayesian Hierarchical Model; Stochastic Frontier Production Finction; Technical Efficiency; Markov Chain Monte Carlo

A stochastic frontier production function has been considered in the case that there is the effect of non-negative technical inefficiency in the data. In the past decades, many studies have been discussed to determine the explanatory variables which affect the technical inefficiency effects in the stochastic frontier production function. We consider a hierarchical model of the stochastic frontier production function to investigate the data which has multiple hierarchical structures. 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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