Forecast combinations in machine learning

• Main Authors: QIU, Yue, XIE, Tian, Jun YU
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Model uncertainty; Machine learning; Nonlinearity; Forecast combinations; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2379; https://ink.library.smu.edu.sg/context/soe_research/article/3378/viewcontent/mlf_v05b_.pdf
• Institution: Singapore Management University

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.