Holder： Junlong Feng(The Hong Kong University of Science and Technology)
Time：2024-04-18 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-product-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a new confidence set under an independence restriction. This confidence set is computationally simple, robust to weak instruments, nonlinearity, and partial identification. In addition, it may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.
About the Speaker:
I am an Assistant Professor of Economics at the Hong Kong University of Science and Technology. I received my Ph.D in Economics from Columbia University in 2020.
My primary research field is econometric theory, with particular interests in nonparametric identification, high dimensional panel data methods, quantile regression, inferential methods for nonlinear models, causal inference, and machine learning.
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