Forecasting with Many Models: Model Confidence Sets and Forecast Combination
Jon Samuels and
Staff Working Papers from Bank of Canada
A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging. We compare different trimming schemes and propose a new one based on Model Confidence Sets that take into account the statistical significance of historical out-of-sample forecasting performance. In an empirical application of forecasting U.S. macroeconomic indicators, we find significant gains in out-of-sample forecast accuracy from our proposed trimming method.
Keywords: Econometric; and; statistical; methods (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Pages: 50 pages
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:13-11
Access Statistics for this paper
More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().