Some Notes about the Using of Software to Estimate the Half-Normal Model

Authors

  • Francisco Javier Ortega Irizo Departamento de Economía Aplicada I Universidad de Sevilla
  • José Manuel Gavilán Ruiz Departamento de Economía Aplicada I Universidad de Sevilla

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2091

Keywords:

Frontera estocástica, frontera determinista, estimador máximo verosímil, software FRONTIER, stochastic frontier, deterministic frontier, maximum likelihood estimator.

Abstract

Using the maximum likelihood method, in order to estimate Half-Normal stochastic frontier production models, entails several practical difficulties that, perhaps, have not been sufficiently emphasised. In employing FRONTIER software, we analyse the case in which the estimation obtained suggests the absence of random factors in the composite error term. We have proved that there are reasons to doubt the validity of the parameter estimates and especially of its standard errors. On the other hand, no estimation is obtained in the previous situation, with LIMDEP software, but an error message.

Downloads

Download data is not yet available.

References

AIGNER, D.J.; CHU, S.F. (1968), “On estimating the industry production function”, American Economic Review, 58, pp. 826–839.

AIGNER, D.J.; LOVELL, C.A.; SCHMIDT, P. (1977), “Formulation and estimation of stochastic frontier production function models”, Journal of Econometrics, 6, pp. 21–37.

BATTESE, G.E.; CORRA, G.S. (1977), “Estimation of a production frontier model: With application to the Pastoral Zone of Eastern Australia”, Australian Journal of Agricultural Economics, 21, pp.169–179.

COELLI, T. (1996), “A guide to FRONTIER version 4.1: a computer program for frontier production function estimation”, CEPA Working Paper 96/07, Department of Econometrics, University of New England, Armidale, Australia: http://www.uq.edu.au/economics/cepa/software/FRONT41-xp1.zip

COELLI, T.J.; RAO, D.S.P.; BATTESE, G.E. (1998), An introduction to efficiency and productivity analysis, Kluver Academic Publishers, Boston.

DAVIDON, W.C. (1991), “Variable metric method for minimization”, SIAM Journal on Optimization, 1, pp. 1–17.

FARRELL, M.J. (1957), “The measurement of productive efficiency”, Journal of the Royal Statistical Society (A), 120, pp. 253–281.

GREENE, W.H. (1993), “The econometric approach to efficiency analysis”, en Fried, H.O.; Lovell, C.A.K.; Schmidt, S.S. (editores), The measurement of productive efficiency: Techniques and applications, Oxford University Press, New York.

MEEUSEN, W.; VAN DEN BROECK, J. (1977), “Efficiency estimation from Cobb-Douglas production functions with composed error”, International Economic Review, 18, pp. 435–444.

ORTEGA, F.J.; BASULTO, J. (2009), “Estimación bayesiana en modelos de producción con frontera determinista”, Estudios de Economía Aplicada, 27 (2), p. 573: http://www.revista-eea.net/documentos/27205.pdf

ORTEGA, F.J.; BASULTO, J.; CAMÚÑEZ, J.A. (2009), “Comparing Bayesian and corrected least-squares estimators in frontier production models”, Boletín de Estadística e Investigación Operativa, 25 (2), pp. 86–96.

ORTEGA, F.J.; GAVILÁN, J.M.; CAMÚÑEZ, J.A. (2010), “Dificultades del estimador máximo verosímil en el modelo de producción Half-Normal con frontera estocástica”, en Anales de Economía Aplicada 2010, Delta Publicaciones, Madrid.

SIMAR L.; WILSON, P.W. (2003), “Statistical inference in non-parametric frontier models: the state of the art”, Journal of Productivity Analysis, 13, pp. 49–78

SIMAR, L. (2007), “How to improve the performances of DEA/FDH estimators in the presence of noise?”, Journal of Productivity Analysis, 28, pp. 183–201.

Published

2016-11-04

How to Cite

Ortega Irizo, F. J., & Gavilán Ruiz, J. M. (2016). Some Notes about the Using of Software to Estimate the Half-Normal Model. Journal of Quantitative Methods for Economics and Business Administration, 11, Páginas 3 a 16. https://doi.org/10.46661/revmetodoscuanteconempresa.2091

Issue

Section

Articles