On winning forecasting competitions in economics
To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. `Causal' models need not win when facing deterministic shifts, a primary factor underlying systematic forecast failure. We derive conditional forecast biases and unconditional (asymptotic) variances to show that when the forecast evaluation sample includes sub-periods following breaks, non-causal models will outperform at short horizons. This suggests using techniques which avoid systematic forecasting errors, including improved intercept corrections. An application to a small monetary model of the UK illustrates the theory.
Volume (Year): 1 (1999)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.springer.com|
Postal:Universidad del País Vasco; DFAE II; Avenida Lehendakari Aguirre, 83; 48015 Bilbao; Spain
Phone: +34 94 6013783
Fax: + 34 94 6013774
Web page: http://spaneconrev.org/
More information through EDIRC
|Order Information:||Web: http://www.springer.com/economics/journal/10108?detailsPage=societies|
When requesting a correction, please mention this item's handle: RePEc:spr:specre:v:1:y:1999:i:2:p:123-160. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.