Model Confidence Sets and forecast combination

Jon D. Samuels and Rodrigo Sekkel

International Journal of Forecasting, 2017, vol. 33, issue 1, 48-60

Abstract: A longstanding finding in the forecasting literature is that averaging the forecasts from a range of 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 approach based on Model Confidence Sets that takes into account the statistical significance of the out-of-sample forecasting performance. In an empirical application to the forecasting of U.S. macroeconomic indicators, we find significant gains in out-of-sample forecast accuracy from using the proposed trimming method.

Keywords: Model combination; Performance-based weighting; Trimming (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:1:p:48-60

DOI: 10.1016/j.ijforecast.2016.07.004

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