Model Confidence Sets and forecast combination

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• 作者：Jon D. Samuels^aAuthor Vitae ; Rodrigo M. Sekkel^b ; ^{rsekkel@bankofcanada.ca}Author Vitae
• 关键词：Model combination ; Performance-based weighting ; Trimming
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：48-60
• 全文大小：561 K
• 卷排序：33

文摘

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.