Academic Research

My research develops econometric methods for settings where conventional asymptotic approximations fail — most of all in the tails of distributions, where the parameter of interest is governed by a handful of extreme observations and standard large-sample theory offers little guidance. Much of this work draws on extreme value theory and fixed-k asymptotics, covering estimation and inference for tail indices, extreme quantiles, threshold models, moment existence, and treatment effects in the tails. I have applied these methods to income and wealth inequality, international trade, health expenditure, macro-financial and currency tail risk, banking, auctions, and binary choice.

Below are my published and working papers, with links to journal versions, arXiv preprints, supplementary material, and slides where available. Comments are always welcome, and I am glad to hear from anyone interested in collaborating.

Working Papers

1. A Simple and Powerful Diagnostic Test for Binary Choice Models. (Joint with Ting Ji, Laura Liu, and Jiahe Xing.) arXiv.

This paper proposes a specification test for the conventional distributional assumptions of error terms in binary choice models, focusing on its tail properties. Based on extreme value theory, we first establish that the tail index of the unobserved error can be recovered by that of the observed covariates. The null hypothesis of the index being zero essentially covers the widely used probit and logit models. We then construct a simple and powerful statistical test for both cross-sectional and panel data, requiring no model estimation and no parametric assumptions. Monte Carlo simulations demonstrate that our test performs well in size and power, and applications to three empirical examples on firm export and innovation decisions and female labor force participation illustrate its general applicability. 

2. Genuinely Robust Inference for Clustered Data. (Joint with Harold D. Chiang and Yuya Sasaki.) arXiv. 

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 for its validity, and show that this condition is frequently violated in practice: specifications from 77% of empirical research articles in American Economic Review and Econometrica during 2020–2021 appear not to meet it. To address this limitation, we propose a genuinely robust inference procedure based on a new cluster score bootstrap. We establish its validity and size control across broad classes of data-generating processes where conventional methods break down. Simulation studies corroborate our theoretical findings, and empirical applications illustrate that employing the proposed method can substantially alter conventional statistical conclusions.

3. Binary Outcome Models with Extreme Covariates: Estimation and Prediction. (Joint with Laura Liu.) Supplementary Material. arXiv.

This paper presents a novel semiparametric method to study the effects of extreme events on binary outcomes and subsequently forecast future outcomes. Our approach, based on Bayes’ theorem and regularly varying (RV) functions, facilitates a Pareto approximation in the tail without imposing parametric assumptions beyond the tail. We analyze cross-sectional as well as static and dynamic panel data models, incorporate additional covariates, and accommodate the unobserved unit-specific tail thickness and RV functions in panel data. We establish consistency and asymptotic normality of our tail estimator, and show that our objective function converges to that of a panel Logit regression on tail observations with the log extreme covariate as a regressor, thereby simplifying implementation. The empirical application assesses whether small banks become riskier when local housing prices sharply decline, a crucial channel in the 2007–2008 financial crisis.

4. High-Dimensional Tail Index Regression. (Joint with Yuya Sasaki and Jing Tao.) arXiv.  

Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts on social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we construct a debiased version of the regularized estimator to facilitate inference and prove its asymptotic normality. Monte Carlo simulation studies corroborate our theoretical findings. We apply these methods (1) to the text analysis of viral posts on X (formerly Twitter), (2) to inference on the effects of maternal smoking on the tail risk of infant birth weight, and (3) to inference on the effects of physical and chronic conditions on abnormally high health expenditures.