Designing multi-model applications with surrogate forecast systems

Leonard A. Smith, Hailiang Du and Sarah Higgins

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target.How is the information in these multiple nonlinear forecast systems best "combined"? Assuming stationarity, in the limit of a very large forecast-outcome archive, each model-based probability density function can be weighted to form a "multimodel forecast" that will, in expectation, provide at least as much information as the most informative single model forecast system. If one of the forecast systems yields a probability distribution that reflects the distribution from which the outcome will be drawn, Bayesian model averaging will identify this forecast system as the preferred system in the limit as the number of forecast-outcome pairs goes to infinity. In many applications, like those of seasonal weather forecasting, data are precious; the archive is often limited to fewer than 26 entries. In addition, no perfect model is in hand. It is shown that in this case forming a single "multimodel probabilistic forecast" can be expected to provemisleading. These issues are investigated in the surrogatemodel (here a forecast system) regime,where using probabilistic forecasts of a simplemathematical systemallowsmany limiting behaviors of forecast systems to be quantified and compared with those undermore realistic conditions.

JEL-codes: C1 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2020-06-01
New Economics Papers: this item is included in nep-ecm and nep-for
References: View complete reference list from CitEc
Citations:

Published in Monthly Weather Review, 1, June, 2020, 148(6), pp. 2233 - 2249. ISSN: 0027-0644

Downloads: (external link)
http://eprints.lse.ac.uk/103275/ Open access version. (application/pdf)
Our link check indicates that this URL is bad, the error code is: 503 Service Unavailable (http://eprints.lse.ac.uk/103275/ [301 Moved Permanently]--> https://eprints.lse.ac.uk/103275/)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:103275

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().