Forecasting with Many Models: Model Confidence Sets and Forecast Combination

Jon Samuels and Rodrigo Sekkel

Staff Working Papers from Bank of Canada

Abstract: 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
Date: 2013
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)

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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:13-11

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