Philippe Goulet Coulombe

oct2020_pic
Job Market Paper

The Macroecononomy as a Random Forest

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.

 

Interests

Econometrics, Machine learning, Macroeconomics, Climate Change.

Email

gouletc@sas.upenn.edu

Download CV

Advisors

Francis X. Diebold

Frank Schorfheide

Job Market Candidate Status
I will be available for interviews at the 2020/2021 job market (AEA/EEA).