EcoSta 2018: Registration
View Submission - EcoSta2018
A0546
Title: Bayesian estimation of dynamic stochastic frontier model: A simulation study Authors:  Chuan Wang - Zhongnan University of Economics and Law (China) [presenting]
Abstract: A stochastic frontier model is proposed that allows for long memory dynamic technical inefficiency structure. We use AR(p) model to explore this temporal behaviour of inefficiency in a panel data setting. We also propose a MCMC method to estimate the lag in the model. To compare the performance of our method with the mainstream Bayesian lag selection method (i.e. Bayes factor), a comprehensive simulation study is conducted.