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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
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/30411

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  1. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
  2. Jordi Galí & Mark Gertler & J. David López-Salido, 2000. "European Inflation Dynamics," Banco de Espa�a Working Papers 0020, Banco de Espa�a.
  3. Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc.
  4. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
  5. 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.
  6. 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, vol. 110(462), pages C1-33, March.
  7. N. Gregory Mankiw & Ricardo Reis, 2002. "What Measure of Inflation Should a Central Bank Target?," NBER Working Papers 9375, National Bureau of Economic Research, Inc.
  8. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, June.
  9. Massimiliano Marcellino, . "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  10. 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.
  11. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  12. 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, vol. 67(1), pages 173-187, May.
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  16. repec:att:wimass:9520 is not listed on IDEAS
  17. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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  19. Duffy, John & McNelis, Paul D., 2001. "Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1273-1303, September.
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