Advanced Search
MyIDEAS: Login to save this article or follow this journal

Nonlinear Forecasting Using a Large Number of Predictors

Contents:

Author Info

  • Alessandro GIOVANNELLI

    ()
    (Universita' di Roma "Tor Vergata")

Abstract

This paper aims to introduce a nonlinear model to forecast macroeconomic time series using a large number of predictors. The technique used to summarize the predictors in a small number of variables is Principal Component Analysis (PC), while the method used to capture nonlinearity is artificial neural network, specifically Feedforward Neural Network (FNN). Commonly in principal component regression forecasts are made using linear models. However linear techniques are often misspecified providing only a poor approximation to the best possible forecast. In an effort to address this issue, the FNN-PC technique is proposed. To determine the practical usefulness of the model, several pseudo forecasting exercises on 8 series of the United States economy, grouped in real and nominal categories, are conducted. This method was used to construct the forecasts at 1-, 3-, 6-, and 12-month horizons for monthly US economic variables using 131 predictors. The empirical study shows that FNN-PC has good ability to predict the variables under study in the period before the start of the "Great Moderation", namely 1984. After 1984, FNN-PC has the same accuracy in forecasting with respect to the benchmark.

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.mulino.it/rivisteweb/scheda_articolo.php?id_articolo=36444
Download Restriction: download restricted to subscribers, see http://www.mulino.it for details

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by SIE - Societa' Italiana degli Economisti (I) in its journal Rivista Italiana degli Economisti.

Volume (Year): 17 (2012)
Issue (Month): 1 (April)
Pages: 143-150

as in new window
Handle: RePEc:rie:review:v:17:y:2012:i:1:n:6

Contact details of provider:
Postal: Piazzale Martelli, 8, 60121 Ancona (Italy)
Email:
Web page: http://www.siecon.org
More information through EDIRC

Order Information:
Web: http://www.mulino.it/edizioni/riviste/scheda_rivista.php?issn=1593-8662

Related research

Keywords: Artificial Neural Networks; Bayesian Regularization; Factor Model; Forecasting; Principal Components Analysis;

Find related papers by JEL classification:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

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:rie:review:v:17:y:2012:i:1:n:6. 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: (SIE).

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.