Forecast combinations in machine learning
Tian Xie () and
Jun Yu ()
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Tian Xie: Shanghai University of Finance and Economics
No 13-2020, Economics and Statistics Working Papers from Singapore Management University, School of Economics
This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and the simple average method, later of which is known as hard to beat in the literature.
Keywords: Model uncertainty; Machine learning; Nonlinearity; Forecast combinations (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
Pages: 46 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for, nep-ore and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2020_013
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