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Temporal Disaggregation of GDP: When Does Machine Learning Help?

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  • Yonggeun Jung

Abstract

We propose a modular framework for temporal disaggregation of quarterly GDP into monthly frequency, in which the regression step accommodates any supervised learning model while Mariano-Murasawa reconciliation enforces quarterly consistency. Comparing Chow-Lin, Elastic Net, XGBoost, and a Multi-Layer Perceptron across four countries, we find that regularization, not nonlinearity, drives the gains: Elastic Net achieves $R^2 = 0.87$ for the United States when lagged indicators are included, while nonlinear models cannot overcome the variance cost of small quarterly samples. We formalize this tradeoff through regime-switching bias and ridge-regularization results.

Suggested Citation

  • Yonggeun Jung, 2025. "Temporal Disaggregation of GDP: When Does Machine Learning Help?," Papers 2506.14078, arXiv.org, revised Apr 2026.
    • Handle: RePEc:arx:papers:2506.14078
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    References listed on IDEAS

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