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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).

Our Distinguished Econometrics and Statistics Faculty

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.

A Network of Support

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.

Scholarly Publications

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.

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

Spotlight on Research

Econometrics and statistics research from our PhD students and faculty is often featured in the pages of Chicago Booth Review.

Inside the Student Experience

Damian Kozbur, PhD ’14, says PhD students at Booth have the flexibility to work on risky problems that no one else has examined.

Current Econometrics and Statistics Students

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.

Program Expectations and Requirements

The Stevens Program at 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.