Stacking Regression for Time-Series, with an Application to Forecasting Quarterly US GDP

Erkal Ersoy, Haoyang Li, Mark Edwin Schaffer, Tibor Szendrei

Research output: Chapter in Book/Report/Conference proceedingChapter


Machine learning methods are being increasingly adopted in economic forecasting. Many learners are available, and a practical issue now presents itself: which one(s) to use? The answer we suggest is ‘stacking regression’ (Wolpert, 1992), an ensemble method for combining predictions of different learners. We show how to use stacking regression in the time series setting. Macroeconomic and financial time series data present their own challenges to forecasting (extreme values, regime changes, etc.), and this presents challenges to
stacking as well. Our findings suggest that using absolute deviations for scoring the base learners performs well compared to stacking on means squared errors. We illustrate this with a Monte Carlo exercise and an empirical application: forecasting US GDP growth around the Covid-19 pandemic.
Original languageEnglish
Title of host publicationOptimal Transport Statistics for Economics and Related Topics
EditorsNgoc Thach Nguyen, Vladik Kreinovich, Thanh Ha Doan, Duc Trung Nguyen
ISBN (Electronic)978-3-031-35763-3
ISBN (Print)978-3-031-35762-6, 978-3-031-35765-7
Publication statusAccepted/In press - 31 Jan 2023

Publication series

NameStudies in Systems, Decision and Control
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190


  • stacking regression
  • machine learning
  • robust statistics
  • forecasting


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