Publication Type

Working Paper

Version

publishedVersion

Publication Date

5-2020

Abstract

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

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

45

Publisher

SMU Economics and Statistics Working Paper Series, Paper No. 13-2020

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Included in

Econometrics Commons

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