A0644
Title: Extreme marginal quantile treatment effect for high dimensional data
Authors: Jing Zhou - University of East Anglia (United Kingdom) [presenting]
Abstract: The estimation and inference of the marginal quantile treatment effects are investigated for high-dimensional data when the quantile level approaches 0 or 1. When the quantile level approaches the ends, quantile regression cannot accurately model the tail distributions. To overcome this limitation, an alternative approach is proposed that uses extreme quantile models to estimate the marginal effect in the presence of a continuous covariate shift. Such models use an extreme value index to model the tail of the distribution function. This method estimates an extreme value index at intermediate quantile levels and extrapolates to the tails where the quantile level is close to zero. By extrapolating, the aim is to estimate the extreme treatment effects consistently and obtain the corresponding asymptotic distribution. Further, to enhance model interpretation, a hypothesis test is proposed to identify the relevant covariates among hundreds of variables for the extreme quantile treatment effects.
