Deus ex Machina? A Framework for Macro Forecasting with Machine Learning

February 28, 2020

Preview Citation

Format: Chicago

Marijn A. Bolhuis, and Brett Rayner. Deus ex Machina? A Framework for Macro Forecasting with Machine Learning, (USA: International Monetary Fund, 2020) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.

Subject: Econometric analysis, Economic forecasting, Factor models, Machine learning, Technology

Keywords: Cross-validation, Ensemble, Factor models, Forecast error, Forecasting method, Forecasts, GDP growth, Global, Machine learning, ML method, ML model, Nowcasting, Random Forest, RF algorithm, SVM regression, Turkey, WP

Publication Details

Pages:

25
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/045
Stock No:

WPIEA2020045
ISBN:

9781513531724
ISSN:

1018-5941

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