I am a Ph.D. candidate in Economics at the University of Wisconsin–Madison, working in econometric theory and statistics. My research develops inference for nonparametric and machine-learning estimators, with a focus on generalized U-statistics, jackknife methods, nearest-neighbor methods, and random forests.
I am advised by Jack R. Porter, Bruce E. Hansen, and Harold D. Chiang.
Website · CV · Research · Teaching
RdistNN, under active development, implements distributional nearest-neighbor methods for nonparametric regression, conditional average treatment effects (CATE), and conditional local average treatment effects (CLATE), with pointwise jackknife inference, diagnostics, plots, and tuning-sensitivity analysis.
- R: RdistNN repository · Documentation and examples
- Stata: RdistNN_stata repository · Documentation and examples
See the software overview for connections to my research and the R replication guide for the conditional Z-estimation paper.
Pointwise inference for conditional moment equations affine in the target, using distributional nearest-neighbor weights and full-sample nuisance predictions without cross-fitting. The theory establishes asymptotic normality and consistency of a frozen-score jackknife under prediction-rate, localization, and deletion-stability conditions.
Jackknife Variance Estimation for Hájek-Dominated Generalized U-Statistics (2025)
Ratio consistency of ordinary and delete-d jackknife variance estimators under Hájek-projection dominance and a weak law for squared first projections. The results cover fixed-order and growing-order generalized U-statistics, and Bernoulli-incomplete statistics under an additional sampling condition.
Full abstracts, citations, presentations, and work in progress are on my research page.
Teaching Assistant at the University of Wisconsin–Madison:
- Principles of Microeconomics (ECON 101) — Fall 2026.
- Introductory Econometrics (ECON 410) — Spring 2024.
- Mathematical Economics I / Ph.D. Math Camp (ECON 703) — Fall 2023.
Handouts and teaching evaluations are available on my teaching page.
Google Scholar · ORCID · arXiv · LinkedIn