How is Machine Learning Useful for Macroeconomic …

How is Machine Learning Useful forMacroeconomic Forecasting? Philippe Goulet Coulombe1 Maxime Leroux2 Dalibor Stevanovic2 St phane Surprenant21 University of Pennsylvania2 Universit du Qu bec Montr alThis version: February 28, 2019 AbstractWe move beyondIs Machine Learning Useful for Macroeconomic Forecasting?by addingthehow. The current forecasting literature has focused on matching specific variables andhorizons with a particularly successful algorithm. To the contrary, we study a wide rangeof horizons and variables and learn about the usefulness of the underlying features driv-ing ML gains over standard macroeconometric methods. We distinguish 4 so-called fea-tures (nonlinearities, regularization, cross-validation and alternative loss function) andstudy their behavior in both the data-rich and data-poor environments.

tion set, and the forecast is made directly using the most recent observables. This is opposed to iterative approach where the model recursion is used to simulate the future path of the variable.4 Also, the direct approach is the only one that is feasible for all ML models.

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