Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach

• Published in: Journal of econometrics Vol. 126; no. 2; pp. 355 - 384
• Main Authors: Kumbhakar, Subal C., Tsionas, Efthymios G.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2005; Elsevier; Elsevier Sequoia S.A
• Series: Journal of Econometrics
• Subjects: Applications; Bayesian analysis; Bayesian method; Commercial banks; Cost function; Costs; Econometrics; Economics; Efficiency; Exact sciences and technology; Insurance, economics, finance; Markov chain Monte Carlo techniques; Markov processes; Markovian processes; Mathematics; Monte Carlo simulation; Nonlinear random effect models and commercial banks; Nonparametric inference; Panel data; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Studies; Technical efficiency; Translog cost function system; United States > US; Nonlinear random effect models and commercial banks; C11; Panel data; C13; Translog cost function system; Technical efficiency; Markov chain Monte Carlo techniques; Bayes estimation; Data analysis; Error estimation; Economic data; Statistical method; Bank(institution); MCMC algorithm; Non linear model; Random effect; Cost function; Econometrics
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2004.05.006

In this paper, we propose simulation-based Bayesian inference procedures in a cost system that includes the cost function and the cost share equations augmented to accommodate technical and allocative inefficiency. Markov chain Monte Carlo techniques are proposed and implemented for Bayesian inferences on costs of technical and allocative inefficiency, input price distortions and over- (under-) use of inputs. We show how to estimate a well-specified translog system (in which the error terms in the cost and cost share equations are internally consistent) in a random effects framework. The new methods are illustrated using panel data on U.S. commercial banks.