Result: A Bayesian approach to imposing curvature on distance functions

Title:
A Bayesian approach to imposing curvature on distance functions
Authors:
O'DONNELL, Christopher J, COELLI, Timothy J
Source:
Current developments in productivity and efficiency mesasurementJournal of econometrics. 126(2):493-523
Publisher Information:
Amsterdam: Elsevier, 2005.
Publication Year:
2005
Physical Description:
print, 1 p.3/4
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Lois de probabilités, Distribution theory, Inférence paramétrique, Parametric inference, Applications, Assurances, économie, finance, Insurance, economics, finance, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme MCMC, MCMC algorithm, Algoritmo MCMC, Algorithme Metropolis Hastings, Metropolis Hastings algorithm, Algoritmo Metropolis Hastings, Analyse donnée, Data analysis, Análisis datos, Chemin de fer, Railway, Ferrocarril, Contrainte inégalité, Inequality constraint, Constreñimiento desigualdad, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Econométrie, Econometrics, Econometría, Effet aléatoire, Random effect, Efecto aleatorio, Efficacité relative, Relative efficiency, Eficacia relativa, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation statistique, Statistical estimation, Estimación estadística, Productivité, Productivity, Productividad, Sciences économiques, Economics, Ciencias económicas, 60J10, 62E17, 62P20, 65C05, 65C40, Fonction distance, Distance function, Modèle effet mixte, Fixed effect model, European railways, Inequality constraints, Markov chain Monte Carlo, Output distance function
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Economics, University of Queensland, St., Lucia, QLD 4072, Australia
ISSN:
0304-4076
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16589521
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.16589521
Database:
PASCAL Archive

Further Information

The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models-a fixed effects model and a random effects model-are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed.