11 citations · 22 across the 14 of their papers we have counts for
19 papers
Building Macroeconomically Relevant Climate Indices via the Assemblage VAR
Christophe Barrette, Philippe Goulet Coulombe, Tim Reinicke
What should a macroeconomically relevant climate index contain? The composition is inherently ambiguous, and aggregation choices affect structural inference. We introduce the Assem…
LGB+: A Macroeconomic Forecasting Road Test
Philippe Goulet Coulombe
Needless to say, linear dynamics are pervasive in economic time series, particularly autoregressive ones. While gradient boosting with trees excels at capturing nonlinearities, it…
Quantifying the Risk-Return Tradeoff in Forecasting
Philippe Goulet Coulombe
Average forecast accuracy is not the same as forecast reliability. I treat forecast loss differentials relative to a benchmark as a return series. I then evaluate these returns usi…
Opening the Black Box of Local Projections
Philippe Goulet Coulombe, Karin Klieber
Local projections (LPs) are widely used in empirical macroeconomics to estimate impulse responses to policy interventions. Yet, in many ways, they are black boxes. It is often uncl…
Ordinary Least Squares as an Attention Mechanism
Philippe Goulet Coulombe
I show that ordinary least squares (OLS) predictions can be rewritten as the output of a restricted attention module, akin to those forming the backbone of large language models. T…
An Adaptive Moving Average for Macroeconomic Monitoring
Philippe Goulet Coulombe, Karin Klieber
The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as inflation. The choice of the look-back window is crucial. Too lo…
Dual Interpretation of Machine Learning Forecasts
Philippe Goulet Coulombe, Maximilian Goebel, Karin Klieber
Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination o…
Maximally Forward-Looking Core Inflation
Philippe Goulet Coulombe, Karin Klieber, Christophe Barrette +1
Timely monetary policy decision-making requires timely core inflation measures. We create a new core inflation series that is explicitly designed to succeed at that goal. Precisely…
From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks
Philippe Goulet Coulombe, Mikael Frenette, Karin Klieber
We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres.…
Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models
Francis X. Diebold, Maximilian Goebel, Philippe Goulet Coulombe
We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. W…
When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume
Francis X. Diebold, Glenn D. Rudebusch, Maximilian Goebel +2
Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea…
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…
Time-Varying Parameters as Ridge Regressions
Philippe Goulet Coulombe
Time-varying parameters (TVPs) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact -- that these are actually ridge regres…
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…
Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach
Francis X. Diebold, Maximilian Göbel, Philippe Goulet Coulombe +2
The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using…