Authors: Mike G. Tsionas
Addresses: Lancaster University Management School, LA1 4YX, UK; Athens University of Economics and Business, Athens, Greece
Abstract: We propose a stochastic frontier model with random coefficients having a flexible distribution. The distribution is modelled non-parametrically. It is shown that maximum likelihood estimation reduces to a fixed-point problem. A fixed-point iteration is proposed and we show that there is a unique regular fixed point. The fixed-point iteration is used in the context of MCMC to perform inferences for all unknown parameters including the optimal support of the distribution of random coefficients.
Keywords: stochastic frontier models; random coefficients; flexible distribution; Bayesian inference; MCMC.
Global Business and Economics Review, 2018 Vol.20 No.1, pp.126 - 139
Received: 22 Nov 2015
Accepted: 26 Apr 2016
Published online: 14 Nov 2017 *