9 papers
Panel Quantile Regression with Common Shocks
Harold D. Chiang, Antonio F. Galvao, Chia-Min Wei
This paper develops an asymptotic and inferential theory for fixed-effects panel quantile regression (FEQR) that delivers inference robust to pervasive common shocks. Such shocks i…
Gaussian approximation for maximum score and non-smooth M-estimators with multiway dependence
Harold D. Chiang, Ahnaf Rafi
The maximum score estimator of Manski (1975) provides an elegant approach to estimate slope coefficient in binary choice models without requiring parametric assumptions on the erro…
Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence
Kaicheng Chen, Harold D. Chiang
This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered depen…
Extremal Quantiles under Two-Way Clustering
Harold D. Chiang, Ryutah Kato, Yuya Sasaki
This paper studies extremal quantiles under two-way clustered dependence. We show that the limiting distribution of unconditional intermediate-order tail quantiles is Gaussian. Thi…
Regression adjustment in completely randomized experiments with many covariates
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper investigates estimation and inference for average treatment effects in completely randomized experiments when researchers observe potentially many covariates. Within Ney…
Empirical Likelihood for Random Forests and Ensembles
Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu
We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty.…