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Artificial Neural Networks

Citations

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Cited by:

  1. Jonathan B. Hill, 2004. "Consistent LM-Tests for Linearity Against Compound Smooth Transition Alternatives," Econometric Society 2004 North American Summer Meetings 42, Econometric Society.
  2. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
  3. Atanas Christev, 2006. "Learning Hyperinflations," Computing in Economics and Finance 2006 475, Society for Computational Economics.
  4. Jason Barr & Francesco Saraceno, 2005. "Modeling the Firm as an Artificial Neural Network," Working Papers Rutgers University, Newark 2005-011, Department of Economics, Rutgers University, Newark.
  5. Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
  6. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
  7. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
  8. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
  9. Heinemann, Maik, 1997. "Convergence of Adaptive Learning and the Concept of Expectational Stability in Linear Rational Expectations Models with Multiple Equilibria," Hannover Economic Papers (HEP) dp-207, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  10. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
  11. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
  12. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
  13. 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.
  14. Constantinos VORLOW & Antonios ANTONIOU & Catherine KYRTSOU, 2004. "Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series," Computing in Economics and Finance 2004 27, Society for Computational Economics.
  15. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La inflación en Colombia: una aproximación desde las redes neuronales," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 20(41-42), pages 143-214, June.
  16. Schuhr Roland, 2004. "Ein Prognose- und Simulationswerkzeug zur Unterstützung der kurzfristigen Personalbedarfsplanung in einem Call Center / A Forecasting and Simulation Tool for Personnel Requirement in a Call Center," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(1-2), pages 166-184, February.
  17. Greg Tkacz & Sarah Hu, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Staff Working Papers 99-3, Bank of Canada.
  18. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
  19. Elena Olmedo & Ricardo Gimeno & Lorenzo Escot & Ruth Mateos, 2007. "Convergencia y Estabilidad de los Tipos de Cambio Europeos: Una Aplicación de Exponentes de Lyapunov," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 44(129), pages 91-108.
  20. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
  21. Marcelo C. Medeiros & Alvaro Veiga, 2003. "Diagnostic Checking in a Flexible Nonlinear Time Series Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(4), pages 461-482, July.
  22. Ignacio Olmeda & Joaquin Pérez, 1995. "Non-linear dynamics and chaos in the Spanish stock market," Investigaciones Economicas, Fundación SEPI, vol. 19(2), pages 217-248, May.
  23. Dan Farhat, 2012. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand," Working Papers 1205, University of Otago, Department of Economics, revised Dec 2012.
  24. Dan Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.
  25. Jozef Baruník, 2008. "How Do Neural Networks Enhance the Predictability of Central European Stock Returns?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(07-08), pages 358-376, Oktober.
  26. Jonathan B. Hill, 2004. "LM-Tests for Linearity Against Smooth Transition Alternatives: A Bootstrap Simulation Study," Econometrics 0401004, University Library of Munich, Germany, revised 05 Jul 2004.
  27. Raimundo Soto, "undated". "El Tipo de Cambio Real de Equilibrio: Un modelo no lineal de Series de Tiempo," ILADES-UAH Working Papers inv094, Universidad Alberto Hurtado/School of Economics and Business.
  28. Gary Madden & Joachim Tan, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," Applied Economics, Taylor & Francis Journals, vol. 40(14), pages 1775-1787.
  29. R. Glen Donaldson & Mark Kamstra, "undated". "Forecasting Fundamental Asset Return Distributions," Computing in Economics and Finance 1997 176, Society for Computational Economics.
  30. Dilip Nachane & Jose Clavel, 2008. "Forecasting interest rates: a comparative assessment of some second-generation nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 493-514.
  31. Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 95-121, June.
  32. Mariano Matilla-Garcia & Carlos Arguello, 2005. "A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 303-308.
  33. Pastorello, Sergio & Patilea, Valentin & Renault, Eric, 2003. "Iterative and Recursive Estimation in Structural Nonadaptive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 449-482, October.
  34. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
  35. Jonathan B. Hill, 2004. "Consistent Model Specification Tests Against Smooth Transition Alternatives," Econometrics 0402004, University Library of Munich, Germany, revised 05 Aug 2005.
  36. Ralf Ostermark & Jaana Aaltonen & Henrik Saxen & Kenneth Soderlund, 2004. "Nonlinear modelling of the Finnish Banking and Finance branch index," The European Journal of Finance, Taylor & Francis Journals, vol. 10(4), pages 277-289.
  37. Boswijk, H.P. & van Dijk, D. & Franses, P.H., 2000. "Asymmetric and Common Abssorbtion of Shocks in Nonlinear Autoregressive Models," CeNDEF Working Papers 00-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  38. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.
  39. Hassan Belkacem Ghassan & Mohammed Souissi & Mohammed Kbiri Alaoui, 2009. "An Alternative Identification of the Economic Shocks in SVAR Models," Economics Bulletin, AccessEcon, vol. 29(2), pages 1019-1026.
  40. Dan Farhat, 2014. "Information Processing, Pattern Transmission and Aggregate Consumption Patterns in New Zealand:," Working Papers 1405, University of Otago, Department of Economics, revised Mar 2014.
  41. Raimundo Soto, "undated". "Nonlinearities in the Demand for money: A Neural Network Approach," ILADES-UAH Working Papers inv107, Universidad Alberto Hurtado/School of Economics and Business.
  42. Arie Preminger & Shinichi Sakata, 2007. "A model selection method for S-estimation," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 294-319, July.
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