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Forecasting inflation with thick models and neural networks

  • McNelis, Paul
  • McAdam, Peter

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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Paper provided by European Central Bank in its series Working Paper Series with number 0352.

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Date of creation: Apr 2004
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Handle: RePEc:ecb:ecbwps:20040352
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  1. 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.
  2. Timothy Cogley & Thomas Sargent, . "Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US," Working Papers 2133503, Department of Economics, W. P. Carey School of Business, Arizona State University.
  3. 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.
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  6. 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.
  7. 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.
  8. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-65, October.
  9. 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.
  10. Mankiw, N. Gregory & Reis, Ricardo, 2002. "What measure of inflation should a central bank target?," Working Paper Series 0170, European Central Bank.
  11. Le Baron, B., 1997. "An Evolutionary Bootstarp Approach to Neural Network Pruning and Generalization," Working papers 9718, Wisconsin Madison - Social Systems.
  12. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods 9804004, EconWPA, revised 04 May 1998.
  13. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  15. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  16. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
  17. Massimiliano Marcellino, . "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  18. Brock, W.A. & Dechert, W.D. & LeBaron, B. & Scheinkman, J.A., 1995. "A Test for Independence Based on the Correlation Dimension," Working papers 9520, Wisconsin Madison - Social Systems.
  19. 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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