The Macroeconomy as a Random Forest
This provides an interpretable machine learning method for economists and policymakers to improve macroeconomic forecasting, though it is incremental as it adapts an existing ML tool to a specific domain.
The authors tackled the problem of forecasting macroeconomic variables by developing Macroeconomic Random Forest (MRF), an algorithm that adapts random forests to model time-varying parameters in linear equations, resulting in clear forecasting gains over alternatives, such as predicting the 2008 unemployment rise and performing well for inflation.
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.