Advanced Search
MyIDEAS: Login

Neural Network Models for Inflation Forecasting: An Appraisal

Contents:

Author Info

  • Ali Choudhary

    (University of Surrey and State Bank of Pakistan)

  • Adnan Haider

    (State Bank of Pakistan)

Abstract

We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a superior predictor while the AR1 model performed better for 21%. Furthermore, arithmetic combinations of several ANN models can also serve as a credible tool for forecasting inflation.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. 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.fahs.surrey.ac.uk/economics/discussion_papers/2008/DP08-08.pdf
Download Restriction: no

Bibliographic Info

Paper provided by School of Economics, University of Surrey in its series School of Economics Discussion Papers with number 0808.

as in new window
Length: 7 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:sur:surrec:0808

Contact details of provider:
Postal: Guildford, Surrey GU2 5XH
Phone: (01483) 259380
Fax: (01483) 259548
Email:
Web page: http://www.surrey.ac.uk/economics/
More information through EDIRC

Related research

Keywords: Artificial Neural Networks; Forecasting; Inflation;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

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.:
as in new window
  1. repec:att:wimass:9530 is not listed on IDEAS
  2. Norman R. Swanson & Halbert White, 1995. "A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Macroeconomics 9503004, EconWPA.
  3. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
  4. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec..
  5. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  6. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:sur:surrec:0808. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alex Mandilaras).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.