33 citations · 124 across the 37 of their papers we have counts for
41 papers
Generic Covariate Adjustment for Regression Discontinuity Designs
Jun Ma, Yuya Sasaki, Zhengfei Yu
It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not…
Inference for High-Dimensional Network Data
Yuya Sasaki, Baoning Zheng
We develop a novel method of inference for network-dependent high-dimensional random vectors. Dependence is characterized via a functional dependence measure based on graph distanc…
Bounds for Standard Errors in Combined Data
Jooyoung Cha, Yuya Sasaki, Nelson Matthew P. Tan
We propose methods for constructing lower bounds on the standard errors of parameters estimated from moment conditions obtained across different samples. Sharp explicit bounds are…
Choosing A Headline Estimand from Matching, DID, and Hybrid Designs: A Minimax-Regret Approach
Yechan Park, Yuya Sasaki
Researchers using panel data to estimate causal effects routinely choose among three approaches to using past outcomes: difference-in-differences (DID), conditioning on lagged outc…
High-Dimensional Tail Index Regression
Yuya Sasaki, Jing Tao, Yulong Wang
Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts in social media, we introduce a high-dimensional tail index regres…
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…
Doubly Robust Estimators with Weak Overlap
Yukun Ma, Pedro H. C. Sant'Anna, Yuya Sasaki +1
Doubly robust (DR) estimators guard against model misspecification but remain sensitive to weak covariate overlap. We show that trimming propensity scores reduces variance but elim…
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…
Extreme Changes in Changes
Yuya Sasaki, Yulong Wang
Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are…
On Uniform Confidence Intervals for the Tail Index and the Extreme Quantile
Yuya Sasaki, Yulong Wang
This paper presents two results concerning uniform confidence intervals for the tail index and the extreme quantile. First, we show that it is impossible to construct a length-opti…
Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method
Yuya Sasaki, Yulong Wang
The conventional cluster-robust (CR) standard errors may not be robust. They are vulnerable to data that contain a small number of large clusters. When a researcher uses the 51 sta…
Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving
Hao Dong, Yuya Sasaki
This paper proposes a density-weighted average derivative estimator based on two noisy measures of a latent regressor. Both measures have classical errors with possibly asymmetric…
Capital and Labor Income Pareto Exponents in the United States, 1916-2019
Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda +1
Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods. Using tabulated summaries of inco…
Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data
Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda +1
Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a…
Non-Existent Moments of Earnings Growth
Silvia Sarpietro, Yuya Sasaki, Yulong Wang
The literature often employs moment-based earnings risk measures like variance, skewness, and kurtosis. However, under heavy-tailed distributions, these moments may not exist in th…
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…
Slow Movers in Panel Data
Yuya Sasaki, Takuya Ura
Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). In the presence of many slow movers, conventional economet…
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…
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400
Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda +1
We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order…
Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments
Xavier D'Haultfoeuille, Stefan Hoderlein, Yuya Sasaki
This paper studies the identification of causal effects of a continuous treatment using a new difference-in-difference strategy. Our approach allows for endogeneity of the treatmen…
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,…