On the Design of Data Sets for Forecasting with Dynamic Factor Models
Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use forecast weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to forecasting euro area, German, and French GDP growth from unbalanced monthly data suggest that both forecast weights and least angle regressions result in improved forecasts. Overall, forecast weights provide yet more robust results.
|Date of creation:||13 Jul 2010|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (+43 1) 798 26 01-0
Fax: (+43 1) 798 93 86
Web page: http://www.wifo.ac.at/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:wfo:wpaper:y:2010:i:376. 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: (Ilse Schulz)
If references are entirely missing, you can add them using this form.