Globally flexible functional forms: The neural distance function
The output distance function is a key concept in economics. However, its empirical estimation often violates properties dictated by neoclassical production theory. In this paper, we introduce the neural distance function (NDF) which constitutes a global approximation to any arbitrary production technology with multiple outputs given by a neural network (NN) specification. The NDF imposes all theoretical properties such as monotonicity, curvature and homogeneity, for all economically admissible values of outputs and inputs. Fitted to a large data set for all US commercial banks (1989-2000), the NDF explains a very high proportion of the variance of output while keeping the number of parameters to a minimum and satisfying the relevant theoretical properties. All measures such as total factor productivity (TFP) and technical efficiency (TE) are computed routinely. Next, the NDF is compared with the Translog popular specification and is found to provide very satisfactory results as it possesses the properties thought as desirable in neoclassical production theory in a way not matched by its competing specification.
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- Hugo Fuentes & Emili Grifell-Tatjé & Sergio Perelman, 2001. "A Parametric Distance Function Approach for Malmquist Productivity Index Estimation," Journal of Productivity Analysis, Springer, vol. 15(2), pages 79-94, March.
- Léopold Simar & Paul W. Wilson, 1998.
"Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models,"
INFORMS, vol. 44(1), pages 49-61, January.
- Simar, L. & Wilson, P.W., "undated". "Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models," CORE Discussion Papers RP 1304, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
- Tim J. Coelli & Chris O'Donnell, 2003. "A Bayesian Approach To Imposing Curvature On Distance Functions," CEPA Working Papers Series WP032003, School of Economics, University of Queensland, Australia.
- Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
- James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks," NBER Working Papers 4718, National Bureau of Economic Research, Inc.
- Gallant, A. Ronald, 1982. "Unbiased determination of production technologies," Journal of Econometrics, Elsevier, vol. 20(2), pages 285-323, November.
- Grosskopf, S. & Margaritis, D. & Valdmanis, V., 1995. "Estimating output substitutability of hospital services: A distance function approach," European Journal of Operational Research, Elsevier, vol. 80(3), pages 575-587, February.
- Bernhard Brümmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
- Kumbhakar, Subal C. & Tsionas, Efthymios G., 2006. "Estimation of stochastic frontier production functions with input-oriented technical efficiency," Journal of Econometrics, Elsevier, vol. 133(1), pages 71-96, July.
- Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
- Tsionas, E.G., 2001. "Stochastic Frontier Models with Random Coefficients," DEOS Working Papers 130, Athens University of Economics and Business.
- Tsionas, E.G., 2001. "Stochastic Frontier Models with Random Coefficients," Athens University of Economics and Business 130, Athens University of Economics and Business, Department of International and European Economic Studies.
- Diewert, Walter E & Wales, Terence J, 1987. "Flexible Functional Forms and Global Curvature Conditions," Econometrica, Econometric Society, vol. 55(1), pages 43-68, January.
- W. Erwin Diewert & T.J. Wales, 1989. "Flexible Functional Forms and Global Curvature Conditions," NBER Technical Working Papers 0040, National Bureau of Economic Research, Inc.
- Efthymios G. Tsionas, 2001. "An introduction to efficiency measurement using Bayesian stochastic frontier models," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 3(2), pages 287-311.
- Gallant, A. Ronald & Golub, Gene H., 1984. "Imposing curvature restrictions on flexible functional forms," Journal of Econometrics, Elsevier, vol. 26(3), pages 295-321, December.
- A. Ronald Gallant & Gene H. Golub, 1982. "Imposing Curvature Restrictions on Flexible Functional Forms," Discussion Papers 538, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Efthymios G. Tsionas & Subal C. Kumbhakar, 2004. "Markov switching stochastic frontier model," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 398-425, December.
- Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
- Efthymios Tsionas, 2000. "Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 13(3), pages 183-205, May.
- Gary Ferrier & Joseph Hirschberg, 1997. "Bootstrapping Confidence Intervals for Linear Programming Efficiency Scores: With an Illustration Using Italian Banking Data," Journal of Productivity Analysis, Springer, vol. 8(1), pages 19-33, March.
- Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
- Santin, Daniel, 2004. "On the Approximation of Production Functions: A Comparison of Artificial Neural Networks Frontiers and Efficiency Techniques," Efficiency Series Papers 2004/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
- Tim Coelli & Sergio Perelman, 2000. "Technical efficiency of European railways: a distance function approach," Applied Economics, Taylor & Francis Journals, vol. 32(15), pages 1967-1976.
- Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
- Reinhard, Stijn & Thijssen, Geert, 1998. "Resource Use Efficiency Of Dutch Dairy Farms; A Parametric Distance Function Approach," 1998 Annual meeting, August 2-5, Salt Lake City, UT 21022, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
- Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
- Léopold Simar & Paul Wilson, 1999. "Some Problems with the Ferrier/Hirschberg Bootstrap Idea," Journal of Productivity Analysis, Springer, vol. 11(1), pages 67-80, February.
- SIMAR, Léopold & WILSON, Paul W., 1997. "Some problems with the Ferrier/Hirschberg bootstrap idea," CORE Discussion Papers 1997062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Tsionas, Efthymios G., 2003. "Combining DEA and stochastic frontier models: An empirical Bayes approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 499-510, June.
- Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
- Fleissig, Adrian R. & Kastens, Terry & Terrell, Dek, 2000. "Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions," Economics Letters, Elsevier, vol. 68(3), pages 235-244, September.
- Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
- Löthgren, Mickael, 1998. "How to Bootstrap DEA Estimators: A Monte Carlo Comparison," SSE/EFI Working Paper Series in Economics and Finance 223, Stockholm School of Economics.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
- Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "The Joint Measurement of Technical and Allocative Inefficiencies: An Application of Bayesian Inference in Nonlinear Random-Effects Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 736-747, September. Full references (including those not matched with items on IDEAS)
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