# How is Machine Learning Useful for Macroeconomic Forecasting?

arXiv: https://arxiv.org/abs/2008.12477 · HTML: https://mlsanity.com/paper/2008.12477/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 18 of 18)

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- [Machine Learning for Economics Research: When What and How?](https://mlsanity.com/paper/2304.00086/) (2023)
- [Econometrics of Machine Learning Methods in Economic Forecasting](https://mlsanity.com/paper/2308.10993/) (2023)
- [Non-linear dimension reduction in factor-augmented vector autoregressions](https://mlsanity.com/paper/2309.04821/) (2023)
- [Machine learning and economic forecasting: the role of international trade networks](https://mlsanity.com/paper/2404.08712/) (2024)
- [Macroeconomic Forecasting and Machine Learning](https://mlsanity.com/paper/2510.11008/) (2025)
- [Slow-Growing Trees](https://mlsanity.com/paper/2103.01926/) (2021)
- [Predicting Inflation with Recurrent Neural Networks](https://mlsanity.com/paper/2104.03757/) (2021)
- [Generalized Autoregressive Score Trees and Forests](https://mlsanity.com/paper/2305.18991/) (2023)
- [Nowcasting with Mixed Frequency Data Using Gaussian Processes](https://mlsanity.com/paper/2402.10574/) (2024)
- [Predictive economics: Rethinking economic methodology with machine learning](https://mlsanity.com/paper/2510.04726/) (2025)
- [Quantifying the Risk-Return Tradeoff in Forecasting](https://mlsanity.com/paper/2605.09712/) (2026)

## Abstract (arXiv)

We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the "treatment" effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice and (iv) the L2 is preferred to the bar ε-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.