Welcome! I am a Ph.D. candidate in economics at Columbia University. My research interest lies in econometrics, applied microeconomics, causal inference and machine learning.
I will be available for interview at 2019 CICE (Beijing), EJM (Rotterdam) and 2020 ASSA (San Diego).
We consider triangular models with a discrete endogenous variable and an instrumental variable (IV) taking on fewer values. Addressing the failure of the order condition, we develop the first approach to restore identification for both separable and nonseparable models in this case by supplementing the IV with covariates, allowed to enter the model in an arbitrary way. For the separable model, we show that it satisfies a system of linear equations, yielding a simple identification condition and a closed-form estimator. For the nonseparable model, we develop a new identification argument by exploiting its continuity and monotonicity, leading to weak sufficient conditions for global identification. Built on it, we propose a uniformly consistent and asymptotically normal sieve estimator. We apply our approach to an empirical application of the return to education with a binary IV. Though under-identified by the IV alone, we obtain results consistent with the literature using our approach. We also illustrate the applicability of our approach via an application of preschool program selection where the supplementation procedure fails.
– Working paper, 2019.
We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical argument to establish consistency without assuming sparsity or the existence of any moments of the error matrix, so that fat-tailed continuous random errors such as Cauchy are allowed. The results are illustrated by simulations.
– Working paper, 2019.
I consider nuclear norm penalized quantile regression for large N and large T panel data models with interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. Uniform rates are obtained for both the regression coefficients and the common component estimators. The rate of the latter is nearly optimal. To derive the rates, I also show new results that establish uniform bounds related to random matrices of jump processes. These results may have independent interest. Finally, I conduct Monte Carlo simulations to illustrate the estimator’s finite sample performance.
- Spring 2017 Econometrics II (M.A.), TA for Professors Ronald Miller. Best TA Award (runner-up).
- Fall 2016 Introduction to Econometrics I (Ph.D.), TA for Professors Jushan Bai. Best TA Award (runner-up).
- Spring 2016 Introduction to Econometrics, TA for Professors Seyhan Erden.
- Fall 2015 Intermediate Microeconomics, TA for Professors Susan Elmes.