  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.
