How is Machine Learning Useful for Macroeconomic Forecasting?

TL;DR

This paper investigates how specific features of machine learning, such as nonlinearity and regularization, contribute to improved macroeconomic forecasting, especially under conditions of high uncertainty and financial stress.

Contribution

It identifies the key features driving ML gains in macroeconomic forecasting and evaluates their effectiveness across different data environments.

Findings

Nonlinearity significantly improves macroeconomic prediction.

Standard factor models remain the best regularization method.

K-fold cross-validation is the most effective validation technique.

Abstract

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 $L_2$ is preferred to the $\bar \epsilon$-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.