Global approximation to arbitrary cost functions: a Bayesian approach with application to US banking

DOI10.1016/j.ejor.2014.08.028zbMath1338.62192OpenAlexW2025056434MaRDI QIDQ299808

Panayotis G. Michaelides, Efthymios G. Tsionas, Konstantinos N. Konstantakis, Angelos T. Vouldis

Publication date: 23 June 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2014.08.028

zbMATH Keywords

neural networkscost functionglobal approximation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15) Production theory, theory of the firm (91B38)

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Cites Work

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• A Bayesian approach to imposing curvature on distance functions
• Unbiased determination of production technologies
• Imposing curvature restrictions on flexible functional forms
• Multilayer feedforward networks are universal approximators
• Data Envelopment Analysis as Nonparametric Least-Squares Regression
• Imposing economic constraints in nonparametric regression: survey, implementation, and extension
• Flexible Functional Forms and Global Curvature Conditions
• Time series forecasting with neural network ensembles: an application for exchange rate prediction
• Artificial neural networks: an econometric perspective^∗
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• Production Function Estimation: Reviving the Primal

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