Posterior analysis of stochastic frontier models using Gibbs sampling
type: Item
instance of: scholarly article
title: Posterior analysis of stochastic frontier models using Gibbs sampling
zbMATH Open document ID: 0938.62114
author: Gary Koop
author: M. F. J. Steel
author: Jacek Osiewalski
published in: Computational Statistics
publication date: 2000-03-02T00:00:00Z
review text: The authors investigate a basic stochastic frontier model of the form \(y_i={x'}_i \beta+\nu_i-z_i,\) where \(y_i\) is the log of output or the negative of log cost for the firm \(i\;(i=1,\dots,N);\) \(x_i\) is a vector of \(K\) exogenous variables; \(v_i\) is a symmetric disturbance capturing measurement error in the stochastic frontier; \(z_i\) is a nonnegative disturbance capturing the level of firm inefficiency. It is assumed that \(v_i\) is \(N(0,\sigma^2).\) Three different specifications for \(z_i\) are considered corresponding to different Gamma distributions with integer shape parameter \(j=1,2,3.\) Bayesian methods are used to analyse this stochastic frontier model. It is shown that Gibbs sampling methods greatly reduce the computational difficulties involved in analyzing such models. The main interest in the empirical illustrations is concentrated on making marginal posterior inference on the parameters and \(z\) and, in particular, on firm efficiency given by \(r_i=\exp (-z_i).\)
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62J12
zbMATH DE Number: 1409708
zbMATH Keywords: Gibbs sampling
zbMATH Keywords: posterior inference
zbMATH Keywords: stochastic frontier models
zbMATH Keywords: empirical economics
reviewed by: M. P. Moklyachuk
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1409708