Gilles Stupfler

Séminaires thématiques
big data and econometrics seminar

Gilles Stupfler

Université d'Angers
Some new perspectives on extremal regression

IBD Salle 21

Îlot Bernard du Bois - Salle 21

5-9 boulevard Maurice Bourdet
13001 Marseille

Mardi 12 mars 2024| 14:00 - 15:30

Michel Lubrano : michel.lubrano[at]
Pierre Michel : pierre.michel[at]


The objective of extremal regression is to estimate and infer quantities describing the tail of a conditional distribution. Examples of such quantities include quantiles and expectiles, and the regression version of the Expected Shortfall. Traditional regression estimators at the tails typically suffer from instability and inconsistency due to data sparseness, especially when the underlying conditional distributions are heavy-tailed. Existing approaches to extremal regression in the heavy-tailed case fall into two main categories: linear quantile regression approaches and, at the opposite, nonparametric approaches. They are also typically restricted to i.i.d. data-generating processes. I will here give an overview of a recent series of papers that discuss extremal regression methods in location-scale regression models (containing linear regression quantile models) and nonparametric regression models. Some key novel results include a general toolbox for extreme value estimation in the presence of random errors and joint asymptotic normality results for nonparametric extreme conditional quantile estimators constructed upon strongly mixing data. Joint work with A. Daouia, S. Girard, M. Oesting and A. Usseglio-Carleve.