Bibliographical references


Álvarez, A. (2001): “La Medición de la Eficiencia y la Productividad”. Ed. Pirámide.

Baker, B. D. (2001): “Can flexible non-linear modelling tell us anything new about educational productivity?”. Economics of Education Review 20 (2001) 81-92.

Bishop, C. M. (1995): “Neural Networks for Pattern Recognition”. Oxford University Press.

Coelli, T. (1996a): “A Guide to DEAP Version 2.1: A Data Envelopment Analysis Program”. Centre for Efficiency and Productivity Analysis (CEPA), Working Paper 96/08.

Coelli, T. (1996b): “A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation”. Centre for Efficiency and Productivity Analysis (CEPA), Working Paper 96/07.

Costa, A y Markellos, R (1997): “Evaluating public transport efficiency with neural networks models”. Transportation Research. Part C. Vol. 5C, Nº5. October.

Cybenko, G. (1988): “Continuous valued neural networks with two hidden layers are sufficient”. Technical Report, Dept. of Computer Science, Tufts University.

Eide, E. y Showalter, M. H. (1998): “The effect of school quality on student performance: A quantile regression approach”. Economics Letters 58, pp. 345-350.

Figlio, D. N. (1999): “Functional form and the estimated effects of school resources”. Economics of Education Review Vol.18. Pp. 241-252.

Fried, H. O., Lovell, C. A. K. and Schmidt, S. S. (1993): “The Measurement of Productive Efficiency”. Oxford University Press, Oxford.

Funahashi, K. (1989): “On the approximate realization of continuous mappings by neural networks 2: 183.

Guermat, C. y Hadri, K. (1999): “Backpropagation Neural Network Versus Translog Model in Stochastic Frontiers: A Monte Carlo Comparison”. Discussion Paper in Economics Nº 99/16, University of Exeter.

Hanushek, E. (1986): “The economics of Schooling”. Journal of Economic Literature. Vol 24, nº3, pp. 1141-1171.

Hashem, S. (1993): “Optimal linear combinations of neural networks”. Doctoral dissertation. University of Purdue.

Hornik, K., Stinchcombe, M. y White, H. (1989): “Multilayer Feed-forward Networks are Universal Approximators”. Neural networks 2, pp. 359-66.

Hornik, K., Stinchcombe, M. and White, H. (1990). “Universal Approximation of an Unknown Mapping and its Derivatives using Multilayer Feed-forward Networks”. Neural Networks 3, pp. 551-60.

Kuan, C. and White, H. (1994): “Artificial neural networks: an econometrics perspective”. Econometrics Review, 13 (1): 1-91.

Lee, T., White, H. and Granger, C. W. J. (1993): “Testing for neglected nonlinearity in time series models: a comparison of neural networks methods and alternative tests”. Journal of Econometrics, 56 (3): 269-91.

Ripley, B. D. (1996): “Pattern Recognition and Neural Networks”. Cambridge. Cambridge University Press.

Rumelhart, D. E.; McClelland, J.L.; and the PDP Research Group (Eds.) (1986): “Parallel Distributed Processing: Explorations on the Microstructure of Cognition, vol.I. Foundations”, MIT Press, Cambridge, MA

Zhang, Y. and Bartels, R. (1998): “The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand”. Journal of Productivity Analysis, 9, 187-204.