A0217
Title: Low-rank panel quantile regression: Estimation and inference
Authors: Yiren Wang - Singapore Management University (Singapore) [presenting]
Abstract: A class of low-rank panel quantile regression models is proposed, allowing for unobserved slope heterogeneity over individuals and time. The heterogeneous intercept and slope matrices are estimated via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debasing. It is shown that the estimators of the factors and factor loadings associated with the slope matrices are asymptotically normally distributed. In addition, two specification tests are developed: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that the true rank of the slope matrix equals one, and the other for the null hypothesis that the slope coefficient exhibits an additive structure under the case that the true rank of slope matrix equals two. The finite sample performance of estimation and inference via Monte Carlo simulations and real datasets are illustrated.
