9 citations · 29 across the 17 of their papers we have counts for
18 papers
Robust Econometrics for Growth-at-Risk
Tobias Adrian, Yuya Sasaki, Yulong Wang
The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel…
Extreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals
Yuya Sasaki, Yulong Wang
We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a…
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…
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…
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…
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…
Error-based Knockoffs Inference for Controlled Feature Selection
Xuebin Zhao, Hong Chen, Yingjie Wang +4
Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the p…
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…
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 Tests of Tail Behavior in Stochastic Frontier Models
William, C. Horrace, Yulong Wang
This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded suppo…
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…
Estimation and Inference about Tail Features with Tail Censored Data
Yulong Wang, Zhijie Xiao
This paper considers estimation and inference about tail features when the observations beyond some threshold are censored. We first show that ignoring such tail censoring could le…
Fixed-k Inference for Conditional Extremal Quantiles
Yuya Sasaki, Yulong Wang
We develop a new extreme value theory for repeated cross-sectional and panel data to construct asymptotically valid confidence intervals (CIs) for conditional extremal quantiles fr…
Threshold Regression with Nonparametric Sample Splitting
Yoonseok Lee, Yulong Wang
This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold. We allow the observations to be cro…
Efficient Minimum Distance Estimation of Pareto Exponent from Top Income Shares
Alexis Akira Toda, Yulong Wang
We propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Our estimator is based on the asymptotic theory of weig…