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Tetsuya Kaji

I am Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. I work at the intersections of economics, statistics, and machine learning.


Working Papers

An Adversarial Approach to Structural Estimation (with E. Manresa and G. Pouliot)

We apply a variant of the machine learning method GAN to estimate structural models with intractable likelihood.

Controlling Tail Risk Measures with Estimation Error (with H. Kang)

Financial risk control inevitably involves estimation of risk. If risk intends to control the probability of bad events, we construct a way to control the “true risk” exploiting the knowledge of estimation error. Such risk measures, named tail risk measures, include Value-at-Risk and expected shortfall. An empirical application controls expected shortfall in portfolio management.

Asymptotic Theory of L-Statistics and Integrable Empirical Processes

Asymptotics of L-statistics can be tricky in some applications. I develop a new way to characterize them using integrable empirical processes and functional delta methods. Bootstrap validity is also shown. Applications to outlier robustness analysis.

Work in Progress

Assessing Outcome-Dependent Heterogeneity in Treatment Effects (with E. Manresa)

Treatment heterogeneity is crucial in policy targeting. First, we interpret the popular heterogeneity measure, the quantile treatment effect, by the principle of equal effects. Second, we relax it to the principle of least effects and propose bounds on subgroup treatment effects. Third, we provide sharp second-order stochastic dominance bounds on the distribution of individual treatment effects.


Theory of Weak Identification in Semiparametric Models

Econometrica, forthcoming. [Code]

Extremal Quantile Regression (with V. Chernozhukov and I. Fernández-Val)

Handbook of Quantile Regression, ed. by R. Koenker, V. Chernozhukov, X. He, and L. Peng, Chapman & Hall/CRC, 2017. [Code]