7 papers
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…
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…
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…