Dernières publications

2 mars 2021

Can Machine Learning Catch the COVID-19 Recession?

Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.

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Parental Decision and Intent Towards COVID-19 Vaccination in Children With Asthma. An Econometric Analysis

Olivier Drouin, Claude Montmarquette, Alexandre Prud'homme, Yann Arnaud, Pierre Fontaine, Roxane Borgès Da Silva et 1 autres auteurs

Covid-19 et Santé

Impact of Tax Reforms in Applied Models: Which Functional Forms Should Be Chosen for the Demand System? Theory and Application for Morocco

Touhami Abdelkhalek et Dorothée Boccanfuso

Économétrie, Évaluation de projets, des programmes et des politiques publiques et Fiscalité et taxation

Attitudes envers la vaccination contre la COVID-19 et niveaux de détresse psychologique de la population du Québec : Analyse des déterminants socio-économiques de ces deux enjeux

Charles Bellemare, Sabine Kröger et Nathalie de Marcellis-Warin

Covid-19, Économie expérimentale et Santé

The Retail Gasoline Price-Fixing Cartel in Québec

Marcel Boyer