# The Macroeconomy as a Random Forest

arXiv: https://arxiv.org/abs/2006.12724 · HTML: https://mlsanity.com/paper/2006.12724/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Can Machine Learning Catch the COVID-19 Recession?](https://mlsanity.com/paper/2103.01201/) (2021)
- [A Neural Phillips Curve and a Deep Output Gap](https://mlsanity.com/paper/2202.04146/) (2022)
- [Are Latent Factor Regression and Sparse Regression Adequate?](https://mlsanity.com/paper/2203.01219/) (2022)
- [Bayesian Neural Networks for Macroeconomic Analysis](https://mlsanity.com/paper/2211.04752/) (2022)
- [Maximally Machine-Learnable Portfolios](https://mlsanity.com/paper/2306.05568/) (2023)
- [SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting](https://mlsanity.com/paper/2211.08661/) (2022)
- [From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks](https://mlsanity.com/paper/2311.16333/) (2023)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [Machine Learning for Economics Research: When What and How?](https://mlsanity.com/paper/2304.00086/) (2023)
- [Non-linear dimension reduction in factor-augmented vector autoregressions](https://mlsanity.com/paper/2309.04821/) (2023)
- [Bayesian Nonlinear Regression using Sums of Simple Functions](https://mlsanity.com/paper/2312.01881/) (2023)
- [Slow-Growing Trees](https://mlsanity.com/paper/2103.01926/) (2021)
- [Generalized Autoregressive Score Trees and Forests](https://mlsanity.com/paper/2305.18991/) (2023)
- [Ordinary Least Squares as an Attention Mechanism](https://mlsanity.com/paper/2504.09663/) (2025)
- [Assessing the informative value of macroeconomic indicators for public health forecasting](https://mlsanity.com/paper/2601.15514/) (2026)

## Abstract (arXiv)

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.