Online Forecast Combination for Dependent Heterogeneous Data

Alessio Sancetta

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

Abstract: This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.

Keywords: Forecast Combination; Model Selection; Multiplicative Update; Non-asymptotic Bound; On-line Learning. (search for similar items in EconPapers)
JEL-codes: C14 C53 (search for similar items in EconPapers)
Pages: 29
Date: 2007-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Note: Ec
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0718.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:0718

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 (jd419@cam.ac.uk).