Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models

The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a computationally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest related to technical inefficiency.

[1]  Léopold Simar,et al.  Pitfalls of Normal-Gamma Stochastic Frontier Models , 1997 .

[2]  J. Geweke,et al.  Bayesian Treatment of the Independent Student- t Linear Model , 1993 .

[3]  Adrian F. M. Smith,et al.  Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms , 1994 .

[4]  E. Tsionas.  Monte Carlo inference in econometric models with symmetric stable disturbances , 1999 .

[5]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[6]  Brian D. Ripley,et al.  Stochastic Simulation , 2005 .

[7]  M. Steel,et al.  Stochastic frontier models: a bayesian perspective , 1994 .

[8]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[9]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[10]  W. Greene.  A Gamma-distributed stochastic frontier model , 1990 .

[11]  Adrian F. M. Smith,et al.  Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .

[12]  Bin Yu,et al.  Looking at Markov samplers through cusum path plots: a simple diagnostic idea , 1998, Stat. Comput..

[13]  M. Steel,et al.  Posterior analysis of stochastic frontier models using Gibbs sampling , 1994 .

[14]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[15]  G. Roberts,et al.  An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms , 1997 .

[16]  P. Bauer.  Recent developments in the econometric estimation of frontiers , 1990 .

[17]  L. Tierney.  Markov Chains for Exploring Posterior Distributions , 1994 .

[18]  R. Stevenson.  Likelihood functions for generalized stochastic frontier estimation , 1980 .

[19]  Efthymios G. Tsionas,et al.  Bayesian analysis of the multivariate poisson distribution , 1999 .

[20]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

[21]  Jacek Osiewalski,et al.  Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models , 1998 .

[22]  L. Wasserman,et al.  Computing Bayes Factors Using a Generalization of the Savage-Dickey Density Ratio , 1995 .

[23]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[24]  Mark F. J. Steel,et al.  On the use of panel data in stochastic frontier models with improper priors , 1997 .