  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.
