7 papers
On Spurious Causality, CO2, and Global Temperature
Philippe Goulet Coulombe, Maximilian Göbel
Stips, Macias, Coughlan, Garcia-Gorriz, and Liang (2016, Nature Scientific Reports) use information flows (Liang, 2008, 2014) to establish causality from various forcings to global…
Slow-Growing Trees
Philippe Goulet Coulombe
Random Forest's performance can be matched by a single slow-growing tree (SGT), which uses a learning rate to tame CART's greedy algorithm. SGT exploits the view that CART is an ex…
How is Machine Learning Useful for Macroeconomic Forecasting?
Philippe Goulet Coulombe, Maxime Leroux, Dalibor Stevanovic +1
We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and ho…
Macroeconomic Data Transformations Matter
Philippe Goulet Coulombe, Maxime Leroux, Dalibor Stevanovic +1
In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology eit…
The Macroeconomy as a Random Forest
Philippe Goulet Coulombe
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 ma…
Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis
Philippe Goulet Coulombe, Maximilian Göbel
On September 15th 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of e…