Combining Forecasts: Can Machines Beat the Average?

26 Pages Posted: 2 Nov 2020

See all articles by Tyler Pike

Tyler Pike

Board of Governors of the Federal Reserve System

Francisco Vazquez-Grande

Board of Governors of the Federal Reserve System

Date Written: September 11, 2020

Abstract

This paper documents benefits of combining forecasts using weights that depend non-linearly of past forecast errors. We propose combining out of sample forecasts from simple models using weights, computed using machine learning algorithms trained on the models' past forecast errors. These nonlinear weights produce more accurate forecasts than conventional approaches based on equal-weighed forecasts, breaking the so-called "forecast combination puzzle''.

Keywords: Forecast Combination; Machine Learning; Non-Linear Models

JEL Classification: C45; C52; C53; C55

Suggested Citation

Pike, Tyler and Vazquez-Grande, Francisco, Combining Forecasts: Can Machines Beat the Average? (September 11, 2020). Available at SSRN: https://ssrn.com/abstract=3691117 or http://dx.doi.org/10.2139/ssrn.3691117

Tyler Pike (Contact Author)

Board of Governors of the Federal Reserve System ( email )

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Washington, DC 20551
United States

Francisco Vazquez-Grande

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States
202-973-7488 (Phone)

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