Partial Identification of Quantile Selection Models
Prof. Songnian CHEN
Tsingshan Chair Professor of Economics
Zhejiang University
Date: 13 November 2025 (Thurday)
Time: 14:30-16:00
Venue: E22-G004
Host: Prof. Degui LI, Distinguished Professor in Business Economics
Abstract
Arellano and Bonhomme (2017) and Chen, Feng and Zhang (2024) considered semiparametric estimation of a binary quantile selection model. Both articles impose a parametric structure on the copula function that characterizes the extent of sample selection bias. However, misspecification of the parametric copula function is likely to result in biased estimates and misleading inference. In this art icle we study partial identification and estimation of the model without imposing any parametric structure on the copula function. We also propose inference procedures, and all of our methods can be implemented in a straightforward manner. Numerical experiments show that our procedures work well. In addition, we also study partial identification and estimation of a quantile regression model subject to a censored selection without imposing any parametric structure on the copula function. As in the case of binary selection, our estimation and inferences procedures for the censored select ion case are easy to implement.
Speaker
Prof. Songnian CHEN received his PhD in Economics from Princeton University in 1994. He then joined the faculty of HKUST. He is currently Tsingshan Chair Professor at Zhejiang University. He was a Chair Professor at HKUST and the National University of Singapore. Prof. Chen’s research interests include theoretical and applied microeconometrics. He has published more than forty articles in Econometrica, Review of Economic Studies, Journal of Econometrics, Econometric Theory, Annals of Statistics, Journal of American Statistical Association and Journal of Business and Economic Statistics. He is a Fellow of Econometric Society and a Fellow of Journal of Econometrics. He served as an Associate Editor of Journal of Econometrics from 2002 to 2021.
All are welcome!
