Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Carta, Alessandro; Steel, Mark F. J. (2009)
Publisher: University of Warwick. Centre for Research in Statistical Methodology
Languages: English
Types: Book
Subjects: QA
The aim of this work is to introduce a new econometric methodology for multi-output production frontiers. In the context of a system of frontier equations, we use\ud a flexible multivariate distribution for the inefficiency error term. This multivariate\ud distribution is constructed through a copula function which allows for separate modelling of the marginal inefficiency distributions and the dependence. We pay specific\ud attention to the elicitation of a sensible (improper) prior and provide a simple sufficient condition for posterior propriety. Inference is conducted through a Markov chain\ud Monte Carlo sampler. We use Bayes factors to compare various copula specifications\ud in the empirical context of Dutch dairy farm data, with two outputs.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44, 182-198.
    • Aigner, D., C. K. Lovell, and P. Schmidt (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6, 21-37.
    • Bauwens, L., M. Lubrano, and J.-F. Richard (1999). Bayesian Inference in Dynamic econometric models. Oxford University Press.
    • Bernardo, J. M. and A. F. M. Smith (2000). Bayesian Theory. John Wiley & Sons.
    • Cherubini, U., W. Vecchiato, and E. Luciano (2004). Copula Methods in Finance. Wiley.
    • Chib, S. and E. Greenberg (1998). Analysis of multivariate probit models. Biometrika 85, 347-361.
    • dos Santos Silva, R. and H. F. Lopes (2008). Copula, marginal distribution and model selection: a Bayesian note. Statistics and Computing 18, 313-320.
    • Embrechts, P., A. McNeil, and D. Straumann (2002). Correlation and dependence in risk management: Properties and pitfalls. In RISK Management: Value at Risk and Beyond, pp. 176-223. Cambridge University Press.
    • Fang, K. T., S. Kotz, and K. Ng (1990). Symmetric multivariate and related distributions. Chapman Hall.
    • Fernández, C., G. Koop, and M. F. J. Steel (2000). A Bayesian analysis of multiple-output production frontiers. Journal of Econometrics 98, 47-79.
    • Fernández, C., G. Koop, and M. F. J. Steel (2002). Multiple-output production with undesirable outputs: An application to nitrogen surplus in agriculture. Journal of the American Statistical Association 97, 432-442.
    • Fernández, C., G. Koop, and M. F. J. Steel (2005). Alternative efficiency measures for multiple-output production. Journal of Econometrics 126, 411-444.
    • Gelfand, A. E., S. K. Sahu, and B. P. Carlin (1995). Efficient parameterizations for normal linear mixed models. Biometrika 82, 479-488.
    • Genest, C. (1987). Frank's family of bivariate distributions. Biometrika 74, 549-55.
    • Geweke, J. (1991). Efficient simulation from the multivariate normal and Student-t distributions subject to linear constraints. In E. M. Keramidas and S. M. Kaufman (Eds.), Computing Science and Statistics: Proceedings of 23rd Symposium on the Interface, pp. 571-578. Interface Foundation of North America.
    • Geweke, J. (1992). Evaluating the accuracy of sampling-based apporaches in the calculation of posterior moments. In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith (Eds.), Bayesian Statistics 4, pp. 169-194. Oxford University Press.
    • Greene, W. H. (2008). Econometric Analysis (sixth ed.). Prentice Hall.
    • Griffin, J. E. and M. F. J. Steel (2008). Flexible mixture modelling of stochastic frontiers. Journal of Productivity Analysis 29, 33-50.
    • Gupta, A. K. and D. K. Nagar (2000). Matrix Variate Distribution. Chapman and Hall.
    • Huard, D., G. Évin, and A.-C. Favre (2006). Bayesian copula selection. Computational Statistics & Data Analysis 51, 809-822.
    • Joe, H. (2005). Asymptotic efficiency of the two-stage estimation method for copula-based models. Journal of Multivariate Analysis 94, 401-419.
    • Joe, H. and J. Xu (1996). The estimation method of inference functions for margins for multivariate models. Technical Report 166, Department of Statistics, University of British Columbia.
    • Jondrow, J., C. Lovell, I. Materov, and P. Schmidt (1982). On the estimation of technical ineffiency in the stochastic frontier model. Journal of Econometrics 19, 233-238.
    • Koop, G., J. Osiewalski, and M. F. J. Steel (1997). Bayesian efficiency analysis through individual effects: Hospital cost frontiers. Journal of Econometrics 76, 77-105.
    • Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris 8, 229-231.
    • Smith, M. D. (2008). Stochastic frontier models with dependent errors components. Econometrics Journal 11, 172-192.
    • Tsionas, E. G. (2000). Full likelihood inference in normal-gamma stochastic frontier models. Journal of Productivity Analysis 13, 183-205.
    • Tsionas, E. G. (2007). Efficiency Measurement with Weibull Stochastic Frontier. Oxford Bulletin of Economics and Statistics 69, 693-706.
    • van den Broeck, J., G. Koop, J. Osiewalski, and M. F. J. Steel (1994). Stochastic frontier models: A Bayesian perspective. Journal of Econometrics 61, 273-303.
    • Zimmer, D. M. and P. K. Trivedi (2006). Using trivariate copulas to model sample selection and treatment effects: Application to family health care demand. Journal of Business and Economic Statistics 24, 63 - 76.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article