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Forecasting inflation with thick models and neural networks

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  • McAdam, Peter
  • McNelis, Paul

Abstract

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 22 (2005)
Issue (Month): 5 (September)
Pages: 848-867

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Handle: RePEc:eee:ecmode:v:22:y:2005:i:5:p:848-867

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Web page: http://www.elsevier.com/locate/inca/30411

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  1. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, Elsevier, vol. 44(2), pages 293-335, October.
  2. Timothy Cogley & Thomas Sargent, . "Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US," Working Papers, Department of Economics, W. P. Carey School of Business, Arizona State University 2133503, Department of Economics, W. P. Carey School of Business, Arizona State University.
  3. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, Elsevier, vol. 67(1), pages 173-187, May.
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  5. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 10(4), pages 561-65, October.
  6. McAdam, Peter & Hughes Hallett, A J, 1999. " Nonlinearity, Computational Complexity and Macroeconomic Modelling," Journal of Economic Surveys, Wiley Blackwell, Wiley Blackwell, vol. 13(5), pages 577-618, December.
  7. Marcellino, Massimiliano, 2002. "Instability and Non-Linearity in the EMU," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3312, C.E.P.R. Discussion Papers.
  8. Blanchard, Olivier & Wolfers, Justin, 2000. "The Role of Shocks and Institutions in the Rise of European Unemployment: The Aggregate Evidence," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 110(462), pages C1-33, March.
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  12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  13. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  14. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, Elsevier, vol. 21(2), pages 323-343, March.
  15. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
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  17. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods, EconWPA 9804004, EconWPA, revised 04 May 1998.
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  19. Peter McAdam & Alpo Willman, 2004. "Supply, Factor Shares and Inflation Persistence: Re-examining Euro-area New-Keynesian Phillips Curves," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(s1), pages 637-670, 09.
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Citations

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Cited by:
  1. Dieppe, Alistair & McAdam, Peter, 2006. "Monetary policy under a liquidity trap: Simulation evidence for the euro area," Journal of the Japanese and International Economies, Elsevier, vol. 20(3), pages 338-363, September.
  2. 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, Taylor & Francis Journals, vol. 12(5), pages 303-308.
  3. Peter McAdam, 2007. "USA, Japan and the Euro Area: Comparing Business-Cycle Features," International Review of Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 21(1), pages 135-156.
  4. Ali Choudhary & Adnan Haider, 2008. "Neural Network Models for Inflation Forecasting: An Appraisal," School of Economics Discussion Papers, School of Economics, University of Surrey 0808, School of Economics, University of Surrey.
  5. McAdam, Peter & Mestre, Ricardo, 2008. "Evaluating macro-economic models in the frequency domain: A note," Economic Modelling, Elsevier, Elsevier, vol. 25(6), pages 1137-1143, November.

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