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

Erkal Ersoy; Haoyang Li; Mark E. Schaffer; Tibor Szendrei

doi:10.1007/978-3-031-35763-3_7

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

Erkal Ersoy, Haoyang Li, Mark E. Schaffer^*, Tibor Szendrei

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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 mean squared error. We illustrate this with a Monte Carlo exercise and an empirical application: forecasting US GDP growth around the Covid-19 pandemic.

Original language	English
Title of host publication	Studies in Systems, Decision and Control
Publisher	Springer
Pages	131-149
Number of pages	19
ISBN (Electronic)	9783031357633
ISBN (Print)	9783031357626
DOIs	https://doi.org/10.1007/978-3-031-35763-3_7
Publication status	Published - 2024

Publication series

Name	Studies in Systems, Decision and Control
Volume	483
ISSN (Print)	2198-4182
ISSN (Electronic)	2198-4190

Keywords

forecasting
machine learning
robust statistics
Stacking regression

ASJC Scopus subject areas

Computer Science (miscellaneous)
Control and Systems Engineering
Automotive Engineering
Social Sciences (miscellaneous)
Economics, Econometrics and Finance (miscellaneous)
Control and Optimization
Decision Sciences (miscellaneous)

Access to Document

10.1007/978-3-031-35763-3_7

Cite this

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abstract = "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 {\textquoteleft}stacking regression{\textquoteright} (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 mean squared error. We illustrate this with a Monte Carlo exercise and an empirical application: forecasting US GDP growth around the Covid-19 pandemic.",

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