The Macroeconomy as a Random Forest

Can Machine Learning Catch the Covid-19 Recession?

“…Figure B1 reports various variable importance (VI) measures for FA-ARRF(2,2) (the reader is referred to Goulet Coulombe, 2020a for numerous implementation details). Universally, the VIs suggest the predominance of other labour indicators like measures of vacancies.…”

“…Macroeconomic random forests. Goulet Coulombe (2020a) proposes a new form of RF better suited for macroeconomic data. The new problem is to extract generalised time-varying parameters (GTVPs) where are the state variables governing time variation and a forest.…”

“…The outputs should be and to be used to split (the parent node) into two children nodes, and . Hence, the greedy algorithm developed in Goulet Coulombe (2020a) runs recursively to construct trees.…”

“…In linear setup when coefficients are shrunk (and maybe selected) to 0, using MARX transform the usual prior into shrinking each to for the lag of predictor . For more sophisticated techniques where shrinkage is only implicit (like RF and boosting), MARX ‘proposes’ the variable-selecting algorithm with pre-assembled group of lags which helps in avoiding that the underlying trees waste splits on a bunch of scattered lags Goulet Coulombe, 2020a). Goulet Coulombe et al (2020) report that the transformation is particularly helpful for U.S. monthly real economic activity targets.…”

“…By using a linear part within the leafs, MRF can extrapolate the same way a linear model does, while retaining the usual benefits of tree-based methods (limited or inexistent overfitting, necessitate little to no tuning, can cope with large data). Goulet Coulombe (2020a) notes that this particular feature gives MRF an edge over RF when it comes to forecasting the (once) extreme escalation of the unemployment rate during the Great Recession.…”