This article presents a formal explanation of the forecast combination puzzle, that simple combinations of point forecasts are repeatedly found to outperform sophisticated weighted combinations in empirical applications. The explanation lies in the effect of finite-sample error in estimating the combining weights. A small Monte Carlo study and a reappraisal of an empirical study by Stock and Watson ["Federal Reserve Bank of Richmond Economic Quarterly" (2003) Vol. 89/3, pp. 71-90] support this explanation. The Monte Carlo evidence, together with a large-sample approximation to the variance of the combining weight, also supports the popular recommendation to ignore forecast error covariances in estimating the weight. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009.
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.
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.
For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Related research
Keywords:
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.)