4 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…
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…
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…