Evaluating ensemble density combination - forecasting GDP and inflation
Forecast combination has become popular in central banks as a means to improve forecasts and to alleviate the risk of selecting poor models. However, if a model suite is populated with many similar models, then the weight attached to other independent models may be lower than warranted by their performance. One way to mitigate this problem is to group similar models into distinct `ensembles'. Using the original suite of models in Norges Bank's system for averaging models (SAM), we evaluate whether forecast performance can be improved by combining ensemble densities, rather than combining individual model densities directly. We evaluate performance both in terms of point forecasts and density forecasts, and test whether the densities are well-calibrated. We find encouraging results for combining ensembles.
|Date of creation:||11 Nov 2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +47 22 31 60 00
Fax: +47 22 41 31 05
Web page: http://www.norges-bank.no/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:bno:worpap:2009_19. 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: ()
If references are entirely missing, you can add them using this form.