This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Forecasting inflation with thick models and neural networks

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Paul McNelis () (Department of Economics, Georgetown University, Washington, DC 20057)
Peter McAdam () (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt/Main, Germany.)

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp352.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by European Central Bank in its series Working Paper Series with number 352.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 33 pages
Date of creation: Apr 2004
Date of revision:
Handle: RePEc:ecb:ecbwps:20040352

Contact details of provider:
Postal: Postfach 16 03 19, Frankfurt am Main, Germany
Phone: +49 69 1344 0
Fax: +49 69 1344 6000
Web page: http://www.ecb.europa.eu/home/html/index.en.html
More information through EDIRC

Order Information:
Postal: Press and Information Division, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany
Email:

For technical questions regarding this item, or to correct its listing, contact: (Official Publications).

Related research
Keywords: Neural Networks; Thick Models; Phillips curves; real-time forecasting; bootstrap.;

Other versions of this item:

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!]
    Other versions:
  2. 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.
    Other versions:
  3. Bondonio, Daniele, 2002. "Evaluating the Employment Impact of Business Incentive Programs in EU Disadvantaged Areas. A case from Northern Italy," P.O.L.I.S. department's Working Papers 27, Department of Public Policy and Public Choice - POLIS. [Downloadable!]
  4. N. Gregory Mankiw & Ricardo Reis, 2003. "What Measure of Inflation Should a Central Bank Target?," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1058-1086, 09. [Downloadable!] (restricted)
    Other versions:
  5. repec:att:wimass:199520 is not listed on IDEAS
  6. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
  8. McAdam, Peter & Hughes Hallett, A J, 1999. " Nonlinearity, Computational Complexity and Macroeconomic Modelling," Journal of Economic Surveys, Blackwell Publishing, vol. 13(5), pages 577-618, December. [Downloadable!] (restricted)
  9. Gali, Jordi & Gertler, Mark & Lopez-Salido, J. David, 2001. "European inflation dynamics," European Economic Review, Elsevier, vol. 45(7), pages 1237-1270. [Downloadable!] (restricted)
    Other versions:
  10. 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. [Downloadable!] (restricted)
    Other versions:
  11. Marcellino, Massimiliano, 2002. "Instability and Non-Linearity in the EMU," CEPR Discussion Papers 3312, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  12. 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. [Downloadable!] (restricted)
    Other versions:
  13. 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. [Downloadable!] (restricted)
    Other versions:
  14. 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.
    Other versions:
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ali Choudhary & Adnan Haider, 2008. "Neural Network Models for Inflation Forecasting: An Appraisal," Department of Economics Discussion Papers 0808, Department of Economics, University of Surrey. [Downloadable!]
  2. Mariano Matilla-García & Carlos Argüello, 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 and Francis Journals, vol. 12(5), pages 303-308, April. [Downloadable!] (restricted)
  3. Peter McAdam, 2007. "USA, Japan and the Euro Area: Comparing Business-Cycle Features," International Review of Applied Economics, Taylor and Francis Journals, vol. 21(1), pages 135-156, January. [Downloadable!] (restricted)
    Other versions:
Statistics
Access and download statistics

Did you know? IDEAS also indexes book chapters.

This page was last updated on 2009-12-1.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.