Working Paper: NBER ID: w30014
Authors: Denis Chetverikov; Yukun Liu; Aleh Tsyvinski
Abstract: In this paper, we introduce the weighted-average quantile regression model. We argue that this model is of interest in many applied settings and develop an estimator for parameters of this model. We show that our estimator is √T-consistent and asymptotically normal with mean zero under weak conditions, where T is the sample size. We demonstrate the usefulness of our estimator in two empirical settings. First, we study the factor structures of the expected shortfalls of the industry portfolios. Second, we study inequality and social welfare dependence on individual characteristics.
Keywords: linear regression; quantile regression; double-debiased machine learning; risk measures; expected shortfall; inequality; social welfare
JEL Codes: C01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| weighted-average quantile regression estimator (C51) | t-consistent (C24) |
| weighted-average quantile regression estimator (C51) | asymptotically normal (C46) |
| financial macro variables (E44) | expected shortfalls of industry portfolios (G32) |
| individual characteristics (Z13) | wage inequality (J31) |
| education (I29) | wages (J31) |