kajitetsuya.github.io

Tetsuya Kaji

I am Associate 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.

CV

Class Material

BUSN 41901 Probability and Statistics

This is a PhD course that introduces fundamental statistical concepts for academic research in business and economics. [Lecture Notes]

Working Papers

Assessing Heterogeneity of Treatment Effects

Treatment effect heterogeneity is important in economics. We introduce bounds on two measures of heterogeneity that complement quantile treatment effects. The first measure is the subgroup treatment effect, which is the average treatment effect for the subgroup defined by a range of Y0. The second measure is the subgroup proportion of winners, which is the share of those whose Y1 is greater than Y0 in the same subgroup. (with Jianfei Cao)

Controlling Tail Risk Measures with Estimation Error

Financial risk control 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. (with Hyungjune Kang)

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.

Publications

An Adversarial Approach to Structural Estimation

Econometrica, 91(6), 2041−2063, November 2023 (with Elena Manresa and Guillaume Pouliot). [Preprint] [Online Appendix] [Code] [Chicago Booth Review]

Metropolis-Hastings via Classification

Journal of the American Statistical Association, 118(544), 2533−2547, 2023 (with Veronika Ročková). [Preprint] [Online Appendix]

Approximate Bayesian Computation via Classification

Journal of Machine Learning Research, 23(350), 1−49, 2022 (with Yuexi Wang and Veronika Ročková). [Preprint]

Adversarial Inference Is Efficient

AEA Papers and Proceedings, 111, 621−625, May 2021 (with Elena Manresa and Guillaume Pouliot). [Online Appendix]

Theory of Weak Identification in Semiparametric Models

Econometrica, 89(2), 733−763, March 2021. [Preprint] [Code]

Extremal Quantile Regression

Handbook of Quantile Regression, ed. by R. Koenker, V. Chernozhukov, X. He, and L. Peng, Chapman & Hall/CRC, 2017, ch. 18, 333-362 (with Victor Chernozhukov and Iván Fernández-Val). [Preprint] [Code]