Unified Unconditional Regression for Multivariate Quantiles, M-Quantiles, and Expectiles

• 成果类型: Article
• 署名作者: Merlo, Luca; Petrella, Lea; Salvati, Nicola; Tzavidis, Nikos
• 署名单位: European University of Rome; Sapienza University Rome; University of Pisa; University of Southampton
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2250512
• 发表日期: 2024
• 页码: 2154-2165
• 关键词: robust estimation; DECOMPOSITION; models

摘要

In this article, we develop a unified regression approach to model unconditional quantiles, M-quantiles and expectiles of multivariate dependent variables exploiting the multidimensional Huber's function. To assess the impact of changes in the covariates across the entire unconditional distribution of the responses, we extend the work of Firpo, Fortin, and Lemieux by running a mean regression of the recentered influence function on the explanatory variables. We discuss the estimation procedure and establish the asymptotic properties of the derived estimators. A data-driven procedure is also presented to select the tuning constant of the Huber's function. The validity of the proposed methodology is explored with simulation studies and through an application using the Survey of Household Income and Wealth 2016 conducted by the Bank of Italy. Supplementary materials for this article are available online.