15 citations · 49 across the 22 of their papers we have counts for
24 papers
Maximum score and non-smooth M-estimators with multiway dependence
Harold D. Chiang, Ahnaf Rafi
Under i.i.d. sampling, the maximum score estimator of Manski (1975) converges at the cube-root rate to a non-Gaussian limit. We show that, under multiway dependence and low-level c…
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…
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…
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.…
Enhancing the Merger Simulation Toolkit with ML/AI
Harold D. Chiang, Jack Collison, Lorenzo Magnolfi +1
This paper develops a flexible approach to predict the price effects of horizontal mergers using ML/AI methods. While standard merger simulation techniques rely on restrictive assu…
Maximal Inequalities for Separately Exchangeable Empirical Processes
Harold D. Chiang
This paper derives new maximal inequalities for empirical processes associated with separately exchangeable random arrays. For fixed index dimension , we establish a global…
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…
Genuinely Robust Inference for Clustered Data
Harold D. Chiang, Yuya Sasaki, Yulong Wang
Conventional cluster-robust inference can be invalid when data contain clusters of unignorably large size. We formalize this issue by deriving a necessary and sufficient condition…
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…
On Using The Two-Way Cluster-Robust Standard Errors
Harold D Chiang, Yuya Sasaki
Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way clu…
Standard errors for two-way clustering with serially correlated time effects
Harold D Chiang, Bruce E Hansen, Yuya Sasaki
We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels. Our proposed estimator and theory allow for arbitrary serial dependence in t…
Dyadic double/debiased machine learning for analyzing determinants of free trade agreements
Harold D Chiang, Yukun Ma, Joel Rodrigue +1
This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning for nuisance parameters estimation when da…
Inference in high-dimensional regression models without the exact or sparsity
Jooyoung Cha, Harold D. Chiang, Yuya Sasaki
This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models. Estimation is based on a combined use of the orthogon…
Multiway empirical likelihood
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood s…
Algorithmic subsampling under multiway clustering
Harold D. Chiang, Jiatong Li, Yuya Sasaki
This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data. We establish a new uniform weak law of large numbers and a new c…
Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
Harold D. Chiang, Kengo Kato, Yuya Sasaki +1
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming,…
Empirical likelihood and uniform convergence rates for dyadic kernel density estimation
Harold D. Chiang, Bing Yang Tan
This paper studies the asymptotic properties of and alternative inference methods for kernel density estimation (KDE) for dyadic data. We first establish uniform convergence rates…
Inference for high-dimensional exchangeable arrays
Harold D. Chiang, Kengo Kato, Yuya Sasaki
We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, w…
Multiway Cluster Robust Double/Debiased Machine Learning
Harold D. Chiang, Kengo Kato, Yukun Ma +1
This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DM…
Lasso under Multi-way Clustering: Estimation and Post-selection Inference
Harold D. Chiang, Yuya Sasaki
This paper studies high-dimensional regression models with lasso when data is sampled under multi-way clustering. First, we establish convergence rates for the lasso and post-lasso…
Post-Selection Inference in Three-Dimensional Panel Data
Harold D. Chiang, Joel Rodrigue, Yuya Sasaki
Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimo…
Many Average Partial Effects: with An Application to Text Regression
Harold D. Chiang
We study estimation, pointwise and simultaneous inference, and confidence intervals for many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling…
Robust Uniform Inference for Quantile Treatment Effects in Regression Discontinuity Designs
Harold D. Chiang, Yu-Chin Hsu, Yuya Sasaki
The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust…
Causal Inference by Quantile Regression Kink Designs
Harold D. Chiang, Yuya Sasaki
The quantile regression kink design (QRKD) is proposed by empirical researchers as a potential method to assess heterogeneous treatment effects under suitable research designs, but…