Before revision: Over the last decades, an impressive amount of non-linearities have been proposed to reconcile reduced-form macroeconomic models with the data. Many of them boil down to have linear regression coefficients evolving through time: threshold/switching/smooth-transition regression; structural breaks and random walk time-varying parameters. While all of these schemes are reasonably plausible in isolation, I argue that those are much more in agreement with the data if they are combined. To this end, I propose Macroeconomic Random Forests, which adapts the canonical Machine Learning (ML) algorithm to the problem of flexibly modeling evolving parameters in a linear macro equation.
After revision: I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) algorithm to the problem of flexibly modeling evolving parameters in a linear macro equation.