Stochastic Frontier Models with Dependent Errors based on Normal and Exponential Margins

Autores/as

  • 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

Palabras clave:

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

Resumen

 

Continuando el reciente trabajo de Gómez-Déniz y Pérez-Rodríguez (2014), el presente artículo extiende los resultados obtenidos a la distribución normal-exponencial con dependencia. En consecuencia, el principal propósito de este artículo es mejorar el modelado de la frontera estocástica tanto de producción como de coste proponiendo para ello una distribución bivariante para errores dependientes que nos permitan encajar los modelos clásicos. Se obtienen las expresiones en forma cerrada para el término de error y la eficiencia técnica. Se ilustra la aplicabilidad del modelo propouesto usando datos reales existentes en la literatura econométrica.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

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.

Descargas

Publicado

2017-07-01

Cómo citar

Gómez-Déniz, E., & Pérez-Rodríguez, J. V. (2017). Stochastic Frontier Models with Dependent Errors based on Normal and Exponential Margins. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 23, Páginas 3 a 23. https://doi.org/10.46661/revmetodoscuanteconempresa.2684

Número

Sección

Artículos