Bayesian Analysis of Stochastic Frontier Models
AbstractIn this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior simulation methods are necessary. Bayesian efficiency measurement with panel data is then discussed. We show how a Bayesian analogue of the classical fixed effects panel data model can be used to calculate the efficiency of each firm relative to the most efficient firm. However, absolute efficiency calculations are precluded in this model and inference on efficiencies can be quite sensitive to prior assumptions. Accordingly, we describe a Bayesian analogue of the classical random effects panel data model which can be used for robust inference on absolute efficiencies. Throughout, we emphasize the computational methods necessary to carry out Bayesian inference. We show how random number generation from well-known distributions is sufficient to develop posterior simulators for a wide variety of models.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 19.
Date of creation: Sep 2004
Date of revision:
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Tim J. Coelli & Chris O'Donnell, 2003.
"A Bayesian Approach To Imposing Curvature On Distance Functions,"
CEPA Working Papers Series
WP032003, School of Economics, University of Queensland, Australia.
- O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
- William Griffiths, 2002. "A Gibbs’ Sampler for the Parameters of a Truncated Multivariate Normal Distribution," Department of Economics - Working Papers Series 856, The University of Melbourne.
- Feng, Guohua & Serletis, Apostolos, 2010.
"Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity,"
Journal of Banking & Finance,
Elsevier, vol. 34(1), pages 127-138, January.
- Guohua Feng & Apostolos Serletis, 2009. "Efficiency, Technical Change, and Returns to Scale in Large U.S. Banks: Panel Data Evidence from an Output Distance Function Satisfying Theoretical Regularity," Monash Econometrics and Business Statistics Working Papers 5/09, Monash University, Department of Econometrics and Business Statistics.
- Kelvin_Balcombe & Dirk_Bezemer & Junior_Davis & Iain_Fraser, 2005. "Livelihoods and Farm Efficiency in Rural Georgia," Development and Comp Systems 0502005, EconWPA.
- C. Charles Okeahalam, 2006. "Production Efficiency in the South African Banking Sector: A Stochastic Analysis," International Review of Applied Economics, Taylor and Francis Journals, vol. 20(1), pages 103-123.
- William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010.
"A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function,"
Department of Economics - Working Papers Series
1092, The University of Melbourne.
- William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Monash Econometrics and Business Statistics Working Papers 3/10, Monash University, Department of Econometrics and Business Statistics.
- Myungsup Kim & Yangseon Kim & Peter Schmidt, 2006.
"On the Accuracy of Bootstrap Confidence Intervals for Efficiency Levels in Stochastic Frontier Models with Panel Data,"
0704, University of Crete, Department of Economics.
- Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
- William Greene, 2008.
"A Stochastic Frontier Model with Correction for Sample Selection,"
08-9, New York University, Leonard N. Stern School of Business, Department of Economics.
- William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
- Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003.
"MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model,"
7, University of Verona, Department of Economics.
- Cappuccio Nunzio & Lubian Diego & Raggi Davide, 2004. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-31, May.
- Erniel B. Barrios & Rouselle F. Lavado, 2010.
"Spatial Stochastic Frontier Models,"
Microeconomics Working Papers
23091, East Asian Bureau of Economic Research.
- Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Hospital efficiency analysis through individual effects: A Bayesian approach," Discussion Paper 1994-47, Tilburg University, Center for Economic Research.
- Supawat Rungsuriyawiboon & Chris O'Donnell, 2004. "Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities," CEPA Working Papers Series WP072004, School of Economics, University of Queensland, Australia.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gina Reddie).
If references are entirely missing, you can add them using this form.