The Econometrics and Statistics Program provides foundational training in the science of learning from data towards solving business problems. Our students engage in extensive collaborative research on cutting-edge theory in Econometrics, Statistics and Machine Learning as well in applied research from a variety of fields within Booth (such as finance, marketing or economics).
Our program builds on a long tradition of research creativity and excellence at Booth.
Our PhD students become active members of the broad, interdisciplinary and intellectually stimulating Booth community.
The program is ideal for students who wish to pursue an (academic) research career in data-rich disciplines, and who are motivated by applications (including but not limited to economics and business).
As our PhD student, you will have a freedom to customize your program by combining courses at Booth (including business-specific areas such as marketing, finance or economics) with offerings at our partnering departments at the University of Chicago (Department of Statistics and Kenneth C. Griffin Department of Economics).
You will acquire a solid foundation in both theory and practice (including learning theory, Bayesian statistics, causal inference or empirical asset pricing).
Chicago Booth’s Econometrics and Statistics faculty are committed to building strong collaborative relationships with doctoral students. We serve as research advisors and career mentors.
Major areas of departmental research include: learning theory; causal inference; machine learning; Bayesian inference; decision theory; graphical models; high dimensional inference; and financial econometrics.
Our PhD students' research has been published in top journals including Econometrica, Journal of Royal Statistical Society, Journal of Econometrics, Neural Information Processing Systems and Journal of Machine Learning Research. Below is a recent list of publications and working papers authored by our PhD students.
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
Tengyuan Liang, Kulunu Dharmakeerthi, and Takuya Koriyama, Transactions on Machine Learning Research, 2835-8856, 2026
Error Estimation and Adaptive Tuning for Unregularized Robust M-Estimator
Pierre C. Bellec, Takuya Koriyama, Journal of Machine Learning Research, 26(16):1−40, 2025
Differentiable Structure Learning and Causal Discovery for General Binary Data
Chang Deng, Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), 2025
Asymptotic Mixed Normality of Maximum Likelihood Estimator for Ewens–Pitman Partition
Takuya Koriyama, Takeru Matsuda and Fumiyasu Komaki, Advances in Applied Probability, 1-21, 2025
Phase Transitions for the Existence of Unregularized M-Estimators in Single Index Models
Takuya Koriyama and Pierre C. Bellec, Proceedings of the 42nd International Conference on Machine Learning (ICML), 258:4141-4149, 2025
Deep Generative Quantile Bayes
Jungeum Kim, Percy S. Zhai, Veronika Ročková, Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (PMLR), 258:4141-4149, 2025
Markov Equivalence and Consistency in Differentiable Structure Learning
Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), 2024
Corrected Generalized Cross-Validation for Finite Ensembles of Penalized Estimators
Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama, Pratik Patil and Kai Tan, Journal of the Royal Statistical Society Series B: Statistical Methodology, 87(2), 289-318, 2024
Joint Trajectory Inference for Single-Cell Genomics Using Deep Learning with a Mixture Prior
Jin-Hong Du, Tianyu Chen, Ming Gao, Jingshu Wang, Proceedings of the National Academy of Sciences, 2024
Fully Data-Driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score
Shunsuke Imai, Takuya Koriyama, Shouto Yonekura, Shonosuke Sugasawa and Yoshihiko Nishiyama, Journal of Business & Economic Statistics 43 (1), 110-121, 2024
Forecasting Using Reference Prices with Exposure Effect
Opher Baron, Chang Deng, Simai He, Hongsong Yuan, Naval Research Logistics, 2024
High-Dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
Boxin Zhao, Percy S. Zhai, Y. Samuel Wang, Mladen Kolar, Electron. J. Statist., 18(1): 1042-1129, 2024
High-Dimensional Sparse Single–Index Regression via Hilbert–Schmidt Independence Criterion
Xin Chen, Chang Deng, Shuaida He, Runxiong Wu, Jia Zhang, Statistics and Computing, 2024
Optimal Estimation of Gaussian (Poly)Trees
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Global Optimality in Bivariate Gradient-based DAG Learning
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar, Advances in Neural Information Processing Systems (NeurIPS), 2023
Optimizing NOTEARS Objectives via Topological Swaps
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar, Proceedings of the 40th International Conference on Machine Learning, 2023
Modeling Tail Index with Autoregressive Conditional Pareto Model
Zhouyu Shen, Yu Chen and Ruxin Shi, Journal of Business and Economic Statistics, (40) 2022
Online Learning to Transport via the Minimal Selection Principle
Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan, Proceedings of 35th Conference on Learning Theory (COLT), (178) 2022
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
Boxin Zhao, Samuel Wang and Mladen Kolar, Journal of Machine Learning Research, (23) 2022
Approximate Bayesian Computation via Classification
Yuexi Wang, Tetsuya Kaji and Veronika Rockova, Journal of Machine Learning Research (In press), 2022
Reversible Gromov-Monge Sampler for Simulation-Based Inference
YoonHaeng Hur, Wenxuan Guo and Tengyuan Liang, Journal of the American Statistical Association (R&R)., 2021.
Data Augmentation for Bayesian Deep Learning
Yuexi Wang, Nicholas Polson and Vadim Sokolov, Bayesian Analysis (In press), 2022
Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
Boxiang Lyu, Zhaoran Wang, Mladen Kolar and Zhuoran Yang, In Proceedings of the 39th International Conference on Machine Learning (ICML), (162) 2022
Optimal Estimation of Gaussian DAG Models
Ming Gao, Wai Ming Tai and Bryon Aragam, International Conference on Artificial Intelligence and Statistics (AISTATS), (151) 2022
Multivariate Change Point Detection for Heterogeneous Series
Yuxuan Guo, Ming Gao, and Xiaoling Lu, Neurocomputing, (510) 2022
Disentangling Autocorrelated Intraday Returns
Rui Da and Dacheng Xiu, Journal of Econometrics (R&R), 2021
When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility
Rui Da and Dacheng Xiu, Econometrica, (89) 2021
Efficient Bayesian Network Structure Learning via Local Markov Boundary Search
Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021
Structure Learning in Polynomial Time: Greedy Algorithms, Bregman Information, and Exponential Families
Goutham Rajendran, Bohdan Kivva, Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021
Variable Selection with ABC Bayesian Forests
Yi Liu, Yuexi Wang and Veronika Rockova, Journal of the Royal Statistical Association: Series B, (83) 2021
A Polynomial-time Algorithm for Learning Non-parametric Causal Graphs
Ming Gao, Yi Ding, and Bryon Aragam, Advances in Neural Information Processing System (NeurIPS), (33) 2020
Uncertainty Quantification for Sparse Deep Learning
Yuexi Wang and Veronika Rockova, International Conference on Artificial Intelligence and Statistics (AISTATS), (2018) 2020
Direct Estimation of Differential Functional Graphical Models
Boxin Zhao, Samuel Wang and Mladen Kolar, Advances in neural information processing systems (NeurIPS), (32) 2019
The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation
Chen Tong, Zhuo Huang, Tianyi Wang, and Cong Zhang, Journal of Empirical Finance (R&R), 2022
Econometrics and statistics research from our PhD students and faculty is often featured in the pages of Chicago Booth Review.
In a recent paper by Chicago Booth’s Stefan Nagel and Dacheng Xiu and Booth PhD student Rui Da, findings suggest that there are limits to statistical arbitrage investment.
Is There a Ceiling for Gains in Machine-Learned Arbitrage?
Three Chicago Booth researchers quantify the likelihood of machine learning leading business executives astray.
How (In)accurate Is Machine Learning?
"If we understand why a black-box method works, we can trust it more with our decisions, explains [Booth's] Ročková, one of the researchers trying to narrow the gap between what’s done in practice and what’s known in theory. "
Would You Trust a Machine to Pick a Vaccine?Booth’s Econometrics and Statistics group has been partnering with several (data science and interdisciplinary) research centers and institutes that facilitate the translation of research into practice. Through these venues, our PhD students can foster a strong research community and find additional resources including elective courses, funding for collaborative student work, and seminars with world-renowned scholars.
Data Science Institute at the University of Chicago
The Data Science Institute executes the University of Chicago’s bold, innovative vision of Data Science as a new discipline by advancing interdisciplinary research, partnerships with industry, government, and social impact organizations.
Center for Applied Artificial Intelligence
The Center for Applied AI incubates transformative projects that fundamentally shape how humans use AI to interact with each other and the world. The Center’s innovative research uses machine learning and behavioral science to investigate how AI can best be used to support human decision-making and improve society.
Toyota Technological Institute at Chicago
Toyota Technological Institute at Chicago (TTIC) is a philanthropically endowed academic computer science institute, dedicated to basic research and graduate education in computer science. Its mission is to achieve international impact through world-class research and education in fundamental computer science and information technology. The Institute is distinctive to the American educational scene in its unique combination of graduate education and endowed research.
The Becker Friedman Institute for Economics
With a mission of turning evidence-based research into real-world impact, the Becker Friedman Institute brings together the University of Chicago’s economic community. Ideas are translated into accessible formats and shared with world leaders tasked with solving pressing economic problems.
Committee on Quantitative Methods in Social, Behavioral and Health Sciences
This is an interdisciplinary community of faculty and students interested in methodological research in relation to applications in social, behavioral, and health sciences. The goals are to create an intellectual niche, exchange research ideas, facilitate research collaborations, share teaching resources, enhance the training of students, and generate a collective impact on the University of Chicago campus and beyond.
The Institute for Data, Econometrics, Algorithms, and Learning
The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments.
The Fama-Miller Center for Research in Finance
Tasked with pushing the boundaries of research in finance, the Fama-Miller Center provides institutional structure and support for researchers in the field.
James M. Kilts Center for Marketing
The Kilts Center facilitates faculty and student research, supports innovations in the marketing curriculum, funds scholarships for MBA and PhD students, and creates engaging programs aimed at enhancing the careers of students and alumni.
Damian Kozbur, PhD ’14, says PhD students at Booth have the flexibility to work on risky problems that no one else has examined.
Damian Kozbur, ’14: 00:01
I went to graduate school in order to develop econometrics tools in conjunction with machine-learning tools in conjunction with economic theory in order to do inference for economic parameters. When you work in high dimensional estimation and you're dealing with problems where the number of variables you're looking at can potentially be in the millions, there's no way to visualize what's going on. Demands now really require that you can handle huge datasets. There's something really satisfying about studying a problem and studying it well. I would say Booth is an excellent place to do it. You have the flexibility to work on really risky problems where you're trying to navigate this landscape that nobody's ever really looked at before. You have an opportunity to dig deeper. You have an opportunity to be rigorous. The faculty is there to help you. They're trying to figure out the same kinds of problems. Things that you figure out cannot always be visualized and it cannot always be easily understood. That doesn't necessarily mean that it's not practical or not useful.
Damian Kozbur, ’14: 01:08
There's an incredible explosion in terms of the amount of data we have on everything. There is an incredible explosion in terms of our understanding of high dimensional econometrics. If you're doing innovative work right now, it will have an impact.
PhD students in econometrics and statistics apply statistical methods to a wide range of business problems, from the effectiveness of machine-learning tools to video-game preferences. Our graduates go on to work in high-profile institutions, generally in academia, finance, or data science.
Yifei Chen
Ruixin Dai
Wenxuan Guo
Shunzhuang Huang
So Won (Sowon) Jeong
Barbara (Basia) Karwowska
Takuya Koriyama
Jiguang Li
Yanlong Liu
Edoardo Marcelli
Bengusu (Bengu) Nar
Chad Schmerling
Bharathan Sundar
Shengjun (Percy) Zhai
The Stevens Doctoral Program at Chicago Booth is a full-time program. Students generally complete the majority of coursework and examination requirements within the first two years of studies and begin work on their dissertation during the third year.
For details, see General Examination Requirements by Area in the Stevens Program Guidebook below.
