Model Averaging for Asymptotically Optimal Combined Forecasts

Yi-Ting Chen () and Chu-An Liu
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Yi-Ting Chen: National Taiwan University, https://www.ntu.edu.tw/english/

No 21-A002, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: We propose a model-averaging (MA) method for constructing asymptotically optimal combined forecasts. The asymptotic optimality is defined in terms of approximating an unknown conditional-mean sequence based on the local-to-zero asymptotics. Unlike existing methods, our method is designed for combining a set of forecast sequences, which is more general than combining a set of single forecasts, generated from a set of predictive regressions. This design generates essential features that are not shared by related existing methods, and the resulting asymptotically optimal weights may be consistently estimated under suitable conditions. We also assess the forecasting performance of our method using simulation data and real data.

Keywords: : Asymptotic optimality; forecast combination; model averaging (search for similar items in EconPapers)
JEL-codes: C18 C41 C54 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2021-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Model averaging for asymptotically optimal combined forecasts (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:sin:wpaper:21-a002

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