Modelos de frontera estocástica con errores dependientes basados en márgenes normal y exponencial

Authors

  • Emilio Gómez-Déniz Department of Quantitative Methods in Economics and TiDES Institute University of Las Palmas de Gran Canaria
  • Jorge V. Pérez-Rodríguez Department of Quantitative Methods in Economics University of Las Palmas de Gran Canaria

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2684

Keywords:

technical and cost efficiencies, stochastic frontier, marginal distribution, dependence, Sarmanov model, eficiencias técnica y de coste, frontera estocástica, distribución marginal, dependencia, modelo de Sarmanov

Abstract

Following the recent work of Gómez-Déniz and Pérez-Rodríguez (2014), this paper extends the results obtained there to the normal-exponential distribution with dependence. Accordingly, the main aim of the present paper is to enhance stochastic production frontier and stochastic cost frontier modelling by proposing a bivariate distribution for dependent errors which allows us to nest the classical models. Closed-form expressions for the error term and technical efficiency are provided. An illustration using real data from the econometric literature is provided to show the applicability of the model proposed.

 

Downloads

Download data is not yet available.

References

Aigner, D., Knox Lovell, C.A. and Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Function Models. Journal of Econometrics, 6, 21-37.

Amblard, C. and Girard, S. (2009). A new extension of bivariate FGM copulas. Metrika, 70, 1-17.

Amsler, C., Prokhorov, A. and Schmidt P. (2016). Endogeneity in stochastic frontier models. Journal of Econometrics, 190:2, 280-288.

Battese, G. and Corra, G. (1977). Estimation of a production frontier model: With application to the pastoral zone of Eastern Australia. Australian Journal of Agricultural Economics, 21:3, 169-179.

Battese, G. E. and T. J. Coelli. (1988). Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics, 38, 387-99.

Bonett, D.G. (2000). Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika, 65:1, 23-28.

Dingman, H.F. and Perry, N.C. (1956). A comparison of the accuracy of the formula for the standard error of Pearson "r" with the accuracy of Fishers z-Transformation. The Journal of Experimental Education, 24:4, 319-321.

El Mehdia, R. and Hafner, C.M. (2014). Inference in stochastic frontier analysis with dependent error terms. Mathematics and Computers in Simulation, 102, 104-116.

Fredricks, G.A. and Nelsen, R.B. (2007). On the relationship between Spearman's rho and Kendall's tau for pairs of continuous random variables. Journal of Statistical Planning and Inference, 137, 2143-2150.

Gómez-Déniz, E. and Pérez-Rodríguez, J.V. (2014). Closed-form solution for a bivariate distribution in stochastic frontier models with dependent errors. Journal of Productivity Analysis, 43:2, 215-223.

Greene, W. (1980a). Maximum likelihood estimation of econometric frontier functions. Journal of Econometrics, 13:1, 27-56.

Greene, W. (1980b). On the estimation of a flexible frontier production model. Journal of Econometrics, 13:1, 101-115.

Greene, W. (1990). A Gamma distributed stochastic frontier model. Journal of Econometrics, 46:1, 141-164.

Greene, W. (2003). Maximum simulated likelihood estimation of the Normal-Gamma stochastic frontier function. Journal of Productivity Analysis, 19:2-3, 179-190.

Jondron, J.; Lovell, C.A.; Materov, I.S. and Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233-238.

Kumbhakar, S.C. and Lovell, C. A. (2000). Stochastic Frontiers Analysis. Cambridge University Press.

Lee, L.-F. (1983). A test for distributional assumptions for the stochastic frontier functions. Journal of Econometrics, 22:3, 245-267.

Lee, T.M.L. (1996). Properties and applications of the Sarmanov family of bivariate distributions. Communications Statistics: Theory and Methods, 25:6, 1207-1222.

Meeusen, W. and Van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review, 18, 435-444.

Park, Y.-H. and Fader, P.S. (2004). Modeling browsing behavior at multiple websites. Marketing Science, 23:3, 280-303.

Sarabia, J.M. and Gómez-Déniz, E. (2011). Multivariate Poisson-Beta distributions with applications. Communications in Statistics: Theory and Methods, 40, 1093-1108.

Sarmanov, O.V. (1966). Generalized normal correlation and two-dimensional Frechet classes. Doklady (Soviet Mathematics), 168, 596-599.

Shubina, M. and Lee, T.M.L. (2004). On maximum attainable correlation and other measures of dependence for the Sarmanov family of bivariate distributions. Communications in Statistics: Theory and Methods, 33:5, 1031-1052.

Smith, M. (2008). Stochastic frontier models with dependent error components. Econometrics Journal, 11, 172-192.

Stevenson, R. (1980). Likelihood functions for generalized stochastic frontier functions. Journal of Econometrics, 13, 57-66.

Tran, K. and Tsionas, M. (2015). Endogeneity in stochastic frontier models: Copulas approach without external instruments. Economics Letters, 133, 85-88.

Wiboonpongse, A.; Liu, J.; Sriboonchitta, S. and Denoeux, T. (2015). Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand. International Journal of Approximate Reasoning, 65, 34-44.

Downloads

Published

2017-07-01

How to Cite

Gómez-Déniz, E., & Pérez-Rodríguez, J. V. (2017). Modelos de frontera estocástica con errores dependientes basados en márgenes normal y exponencial. Journal of Quantitative Methods for Economics and Business Administration, 23, Páginas 3 a 23. https://doi.org/10.46661/revmetodoscuanteconempresa.2684

Issue

Vol. 23 (2017)

Section

Articles

License

Copyright (c) 2017 Journal of Quantitative Methods for Economics and Business Administration

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data). 

All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.

Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.