Theme：Distributional Effects in Censored Quantile Regressions with Endogeneity and Heteroskedasticity

Guest Speaker：Xi Wang (Jinan University)

Time：May 6th, 2026(Wednesday)，10:30-11:45 am

Venue：Room 318, Business School Building

Organizer：The Department of Economics

Guest Bio:

  Xi Wang is an Associate Professor at Jinan University. He received his Ph.D. from the Hong Kong University of Science and Technology. His research focuses on econometric theory and applications. He has published in journals such as Journal of Econometrics, Econometric Theory, Oxford Bulletin of Economics and Statistics, Econometric Journal, and Marketing Letters.

Synopsis：

  Distributional effects, captured by quantile frameworks, are well-received for characterizing heterogeneous impacts of economic factors across the unobserved relative ranks. Censored outcome, endogenous regressor and heteroskedastic error are prevalent in empirical work, yet challenge the consistency of existing quantile estimation methods.

 This paper proposes a two-nested-step (TNS) estimation method for distributional effects in censored quantile models with endogeneity and heteroskedasticity. It combines the sequential analysis with the control function approach, adapting for heterogeneous distributional effects.

 The estimation algorithm is a two-step procedure nested with a sequence of series quantile regressions, thereby providing applied researchers with a computationally tractable and practically feasible tool. Monte Carlo simulation results demonstrate the good performance of our estimator in a finite sample. We apply the proposed method to estimate heterogeneous income elasticities of households across relative ranks of commodity expenditure using data from the UK Family Expenditure Survey.