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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.
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    • 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.
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