WANG YIREN

Professor Yichong ZHANG
Email: yczhang@smu.edu.sg
Tel: +65 68280881

Professor SU Liangjun
Email: sulj@sem.tsinghua.edu.cn
Tel: +86 10 62789506

Email: peter.phillips@yale.edu
Tel: (203) 432-3695

Professor Jia LI
Email: jiali@smu.edu.sg
Tel: +65 68280890

WORKING PAPERS

"Panel Data Models with Time-Varying Latent Group Structures",  with Peter C.B. Phillips and Liangjun Su (Job Market Paper)

This paper considers a linear panel model with interactive fixed effects such that individual heterogeneity is captured by latent group structure and time heterogeneity is captured by an unknown structural break. We allow the model to have different numbers of groups and/or different group memberships before and after the break. With the preliminary estimates by nuclear norm regularization followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. We show that the break point, the number of groups and the group membership can be estimated correctly with probability approaching one. Finite sample performance of the methodology is illustrated via Monte Carlo simulations and a real dataset application.

"Low-rank Panel Quantile Regression: Estimation and Inference",  with Liangjun Su and Yichong Zhang (Submitted to Annals of Statistics)

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: 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 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. We also illustrate the finite sample performance of estimation and inference via Monte Carlo simulation and real datasets.

WORK IN PROGRESS

"Inference for Quantile Factor Models"