High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing

no code implementations • 4 Dec 2019 • Alexandre Belloni, Mingli Chen, Oscar Hernan Madrid Padilla, Zixuan, Wang

We propose a generalization of the linear panel quantile regression model to accommodate both \textit{sparse} and \textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings.

regression