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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. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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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. Ali Choudhary & Adnan Haider, 2008. "Neural Network Models for Inflation Forecasting: An Appraisal," School of Economics Discussion Papers 0808, School of Economics, University of Surrey.
  2. McAdam, Peter, 2003. "US, Japan and the euro area: comparing business-cycle features," Working Paper Series 0283, European Central Bank.
  3. 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.
  4. McAdam, Peter & Mestre, Ricardo, 2008. "Evaluating macro-economic models in the frequency domain: A note," Economic Modelling, Elsevier, vol. 25(6), pages 1137-1143, November.
  5. 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.

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