33 citations · 124 across the 37 of their papers we have counts for
30 papers · 1 filter
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…
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…
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…
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…
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,…
Welfare Analysis via Marginal Treatment Effects
Yuya Sasaki, Takuya Ura
Consider a causal structure with endogeneity (i.e., unobserved confoundedness) in empirical data, where an instrumental variable is available. In this setting, we show that the mea…
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…
Unconditional Quantile Regression with High Dimensional Data
Yuya Sasaki, Takuya Ura, Yichong Zhang
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the un…
Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators
Yuya Sasaki, Yulong Wang
Common approaches to inference for structural and reduced-form parameters in empirical economic analysis are based on the consistency and the root-n asymptotic normality of the GMM…
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…