Research Interest: My research focuses on developing robust econometric methods to address challenging empirical issues, including models with limited identification information, model misspecification, strong cross-sectional or time-series dependence, and high-dimensional estimation and inference with machine learning methods. These methods are applied across diverse fields such as labor economics, industrial organization, macroeconomics, and finance.

Working Papers

“Optimal Estimation of Two-Way Effects under Limited Mobility”
(with Sheng Chao Ho and Frank Schorfheide). arXiv:2506.21987.
Revise and Resubmit, Econometrica. This Version: June 2025

Abstract: We propose an empirical Bayes estimator for two-way effects in linked data sets based on a novel prior that leverages patterns of assortative matching observed in the data. To capture limited mobility we model the bipartite graph associated with the matched data in an asymptotic framework where its Laplacian matrix has small eigenvalues that converge to zero. The prior hyperparameters that control the shrinkage are determined by minimizing an unbiased risk estimate. We show the proposed empirical Bayes estimator is asymptotically optimal in compound loss, despite the weak connectivity of the bipartite graph and the potential misspecification of the prior. We estimate teacher values-added from a linked North Carolina Education Research Data Center student-teacher data set.

“How to Weight in Moments Matching: An ML Approach with Applications to Earnings Dynamics”
(with Alejandro Sanchez-Becerra and Andrew Shephard).
Revise and Resubmit, The Review of Economic Studies. This Version: January 2026

Abstract: Following the seminal paper by Altonji and Segal (1996), empirical studies commonly adopt equal or diagonal weighting in minimum distance estimation to mitigate finite-sample bias arising from sampling error in the weighting matrix. We propose a new weighting scheme that combines cross-fitting with regularized estimation of the weighting matrix, in the spirit of de-biased machine learning. We also propose a new formula for cross-fitted standard errors. We show that several canonical models in the earnings dynamics literature satisfy exact or approximate sparsity conditions that can be exploited by graphical lasso estimation of the weighting matrix. Within a many-moment asymptotic framework, we characterize the asymptotic distribution of the structural parameters. Extensive simulation studies demonstrate that our approach outperforms commonly used alternative weighting schemes. Finally, an empirical application using data from the Panel Study of Income Dynamics illustrates the practical gains of our method.

“Clustering for Multi-Dimensional Heterogeneity with an Application to Production Function Estimation”
(with Peng Shao and Frank Schorfheide).
Conditionally Accepted, Quantitative Economics, This Version: June 2025

Abstract: This paper studies the estimation of multi-dimensional heterogeneous parameters in a nonlinear panel data model with endogeneity. These heterogeneous parameters are modeled with group patterns. Through estimating multiple memberships for each unit, the proposed method is robust to limited information from a subset of clusters: either due to sparse interactions of characteristics or weak identification of some combinations of heterogeneous parameters. We estimate the memberships along with the group-specific and common parameters in a nonlinear GMM framework and derive their large sample properties. Finally, we apply this approach to the estimation of heterogeneous firm-level production functions parameters which are converted into markup estimates.

Publications:

“Identifying the Volatility Risk Price Through the Leverage Effect” (with Eric Renault and Paul Sangrey)   Supplemental Appendix
Journal of Econometrics, 2025, 248

“Macro-Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models” (with Winston Dou and Zhipeng Liao)   Supplemental Appendix ; Additional Materials
Econometrica, 2022, 90(2), 685–713

“Instrumental Variable Estimation of Structural VAR Models with Possible Non-stationarity” (with Xu Han and Atsushi Inoue)
Econometric Theory, 2022, 38(5), 845-874.

“Generic Results for Establishing the Asymptotic Size of Confidence Sets and Tests” (with Donald Andrews & Patrik Guggenberger )
Journal of Econometrics, 2020, 218(2), 496-531

“On Uniform Asymptotic Risk of Averaging GMM Estimator” (with Zhipeng Liao & Ruoyao Shi)
Quantitative Economics, 2019, 931–979

“Shrinkage Estimation of High-Dimensional Factor Models with Structural  Instabilities” (with Zhipeng Liao & Frank Schorfheide)   Data and Matlab Programs
The Review of Economic Studies, 2016, 83(4), 1511-1543.

“Comment on ‘In-sample Inference and Forecasting in Misspecified Factor Models’” (with Bruce Hansen),
Journal of Business and Economic Statistics, 2016, 34(3), 345-347

“Robust Inference in Nonlinear Models with Mixed Identification Strength”
Journal of Econometrics, 2015, 189(1), 207-228.

“Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach (with Bruce Hansen)
Journal of Econometrics, 2015, 186(2), 280-293.

“Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with  Many Moments” (with Zhipeng Liao)     Supplemental Appendix
Journal of Econometrics, 2015, 186(2), 443-464.

“GMM Estimation and Uniform Subverter Inference with Possible Identification  Failure” (with Donald Andrews)    Supplemental Appendix
Econometric Theory, 2014, 30(2), 287-333.

“Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure (with Donald Andrews)   Supplemental Appendix
Journal of Econometrics, 2013, 173(1), 36-56.

“Estimation and Inference with Weak, Semi-strong, and Strong Identification” (with Donald Andrews)    Supplemental Appendix; Correction
Econometrica, 2012, 80(5), 2153-2211.

“Cointegrating Rank Selection in Models with Time-Varying Variance” (with Peter Phillips)
Journal of Econometrics, 2012, 169(2), 155-165.

“Semiparametric Cointegrating Rank Selection” (with Peter Phillips)
The Econometrics Journal, 2009, 12(1), 83-104.