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Forecasting inflation with thick models and neural networks

  • McAdam, Peter
  • McNelis, Paul

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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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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  1. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December.
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  18. McAdam, Peter & Hughes Hallett, A J, 1999. " Nonlinearity, Computational Complexity and Macroeconomic Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 577-618, December.
  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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