Title: Stochastic frontier models with flexible random coefficients

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.

DOI: 10.1504/GBER.2018.088487

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 *

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