Forecasting Distributions with Experts Advice

Alessio Sancetta

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: This paper considers forecasts of the distribution of data whose distribution function is possibly time varying. The forecast is achieved via time varying combinations of experts’ forecasts. We derive theoretical worse case bounds for general algorithms based on multiplicative updates of the combination weights. The bounds are useful to study the properties of forecast combinations when data are nonstationary and there is no unique best model. An application with an empirical study is used to highlight the results in practice.

Keywords: Expert; Forecast Combination; Multiplicative Update; Non-asymptotic Bound; On-line Learning; Shifting. (search for similar items in EconPapers)
JEL-codes: C14 C53 (search for similar items in EconPapers)
Pages: 30
Date: 2005-05
Note: EM
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0517.pdf (application/pdf)

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:cam:camdae:0517

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().