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The Macroeconomy as a Random Forest

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  • Philippe Goulet Coulombe

    (University of Pennsylvania)

Abstract

I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation. Its main output, Generalized Time-Varying Parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, structural breaks/change) and allowing for sophisticated new ones. The approach delivers clear forecasting gains over numerous alternatives, predicts the 2008 drastic rise in unemployment, and performs well for inflation. Unlike most ML-based methods, MRF is directly interpretable - via its GTVPs. For instance, the successful unemployment forecast is due to the influence of forward-looking variables (e.g., term spreads, housing starts) nearly doubling before every recession. Interestingly, the Phillips curve has indeed flattened, and its might is highly cyclical.

Suggested Citation

  • Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    • Handle: RePEc:bbh:wpaper:21-05
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    Cited by:

    1. Philippe Goulet Coulombe, 2025. "Ordinary Least Squares as an Attention Mechanism," Papers 2504.09663, arXiv.org.
    2. Philippe Goulet Coulombe & Karin Klieber, 2025. "An Adaptive Moving Average for Macroeconomic Monitoring," Papers 2501.13222, arXiv.org.
    3. Maximilian Gobel & Tanya Araújo, 2020. "Indicators of Economic Crises: A Data-Driven Clustering Approach," Working Papers REM 2020/0128, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    4. Alina Landowska & Robert A. K{l}opotek & Dariusz Filip & Konrad Raczkowski, 2025. "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest," Papers 2504.20993, arXiv.org.
    5. Philippe Goulet Coulombe & Karin Klieber, 2025. "Opening the Black Box of Local Projections," Papers 2505.12422, arXiv.org.
    6. Philippe Goulet Coulombe & Maximilian Göbel, 2021. "On Spurious Causality, CO 2 , and Global Temperature," Econometrics, MDPI, vol. 9(3), pages 1-18, September.
    7. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    8. Sun, Weixin & Wang, Yong & Zhang, Li & Chen, Xihui Haviour & Hoang, Yen Hai, 2025. "Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment," Technological Forecasting and Social Change, Elsevier, vol. 215(C).

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