The Macroeconomy as a Random Forest

TL;DR

The paper introduces Macroeconomic Random Forest (MRF), a flexible machine learning model that captures evolving macroeconomic parameters, improves forecasts, and offers interpretability through its generalized time-varying parameters.

Contribution

It develops a novel ML algorithm tailored for macroeconomic modeling, capable of capturing nonlinearities and structural changes while maintaining interpretability.

Findings

MRF outperforms traditional models in forecasting accuracy.

Successfully predicted the 2008 unemployment spike.

Revealed the cyclical nature of the Phillips curve.

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.