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Data Science for Economics

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Traditionally data analyses in economics have focused on answering causal questions. Although this is not true universally, many of the most pressing questions in empirical economics concern causal questions, such as the impact, both short and long run, of educational choices on labor market outcomes, and of economic policies on distributions of outcomes. This makes them conceptually quite different from the predictive type of questions that many of the recently develop methods in machine learning are primarily designed for. Often these questions involve deliberate treatment choices (e.g., educational choices, or price decisions) by individuals or firms intended to optimize outcomes, so that causal effects cannot simply be learned by comparing similar treated and control units. In addition inference plays a more important role than in prediction problems. Nevertheless, there are often predictive components to the models economists use where the predictive tools developed by computer scientists and statisticians can be used after being adapted to the specific context. With many economists now using large scale administrative data, from government (e.g., the work by Raj Chetty in the economics department using Internal Revenue Service data), and private companies (e.g., supermarket data, in the work by Susan Athey from the Stanford GSB and David Blei), text data (e.g., in the work by Matt Gentzkow in studies of polarization and the media), and methods for optimizing medical decisions (Mohsen Bayati at the gsb)these methods are becoming increasingly popular, and demand for more sophisticated methods that take account of the causal nature of these questions and the richness of the data is growing.

Contributors

Susan Athey

Mohsen Bayati

Nadarajan Chetty

Guido Imbens

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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).

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.

Bryon Aragam

Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar

Nabarun Deb

Assistant Professor of Econometrics and Statistics

Christian B. Hansen

Wallace W. Booth Professor of Econometrics and Statistics

Tetsuya Kaji

Associate Professor of Econometrics and Statistics and Richard Rosett Faculty Fellow

Tengyuan Liang

Professor of Econometrics and Statistics and William Ladany Faculty Fellow

Nicholas Polson

Robert Law, Jr. Professor of Econometrics and Statistics

Veronika Rockova

Professor of Econometrics and Statistics, and James S. Kemper Faculty Scholar

Jeffrey R. Russell

Alper Family Professor of Econometrics and Statistics

Ekaterina (Katja) Smetanina

Assistant Professor of Econometrics and Statistics and Asness Junior Faculty Fellow

Panagiotis Toulis (Panos)

Associate Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow

Dacheng Xiu

Professor of Econometrics and Statistics

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.

Is There a Ceiling for Gains in Machine-Learned Arbitrage?

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.

How (In)accurate Is Machine Learning?

Three Chicago Booth researchers quantify the likelihood of machine learning leading business executives astray.

Would You Trust a Machine to Pick a Vaccine?

"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. "

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.

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.

Video Transcript

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.

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.

Current Students

Y ifei Chen Ruixin Dai

Wenxuan Guo

Shunzhuang Huang So Won (Sowon) Jeong Takuya Koriyama

Jiguang Li Yanlong Liu Edoardo Marcelli Bengusu (Bengu) Nar Chad Schmerling

Zhouyu Shen

Shengjun (Percy) Zhai

Current Students in Sociology and Business

Jacy Anthis

Program Expectations and Requirements

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.

Download the 2024-25 Guidebook!

Economics and Data Analysis (DEDA)

Doctor of Economics and Data Analysis

A career accelerator for future leaders in applied economics.

The Doctor of Economics and Data Analysis (DEDA) is a groundbreaking interdisciplinary program that transcends traditional economic theory by blending in-depth knowledge of data science and applied economics with real-world problem-solving. With an emphasis on translational research and interdisciplinary collaboration, DEDA sets itself apart from other economics programs. Beyond developing technical skills, the program highlights the importance of leadership and collaboration, preparing you to communicate effectively with diverse audiences and lead multidisciplinary teams in making data-driven decisions and implementing innovative solutions to complex economic and societal challenges.

The DEDA program offers a career-friendly, non-dissertation-based degree tailored for working professionals seeking to advance their careers in economics and the analysis of economic data. With options for online learning and intensive in-person experiences, the curriculum offers flexibility for working professionals. Our goal is to cultivate expertise in data analytics, promote leadership and innovation, and hone practical problem-solving skills. By equipping students with these essential tools, the DEDA program prepares them to become future leaders across the public, private, and nonprofit sectors.

Program Highlights

• Career Accelerator: Unlike traditional PhD programs, DEDA is a non-dissertation program designed for professionals looking to establish or advance their careers outside of academia. The transdisciplinary approach provides invaluable experience working with experts from a variety of backgrounds, just as you do in professional settings.
• Purpose-Built Curriculum: The DEDA curriculum not only provides the most advanced training in economics and data analytics, but also teaches practical application by developing communication and leadership skills.
• Real-World Impact: The DEDA program is designed to equip you with the skills to address complex economic issues, foster innovation, and drive positive change. This is achieved by emphasizing the application of economic principles and data analytics to real-world situations, while encouraging interdisciplinary collaboration and problem-solving.

Program At-a-glance

required units

degree awarded

Spring, Summer, Fall

program start

3 years | full time*

estimated completion time

C. Mónica Capra

Professor of Economic Sciences

Research Interests

Experimental Economics, Behavioral Economics, Neuroeconomics

Mark Abdollahian

Full Clinical Professor

Strategic Decision Making, International Political Economy, Sustainable Development

Gregory DeAngelo

Associate Professor of Economic Sciences Director, Computational Justice Lab

Criminal Justice, Law, Applied Econometrics

Robert Klitgaard

University Professor

Public Policy, Economic Strategy, Institutional Reform, Corruption

Assistant Professor

American Politics, Racial and Ethnic Politics, Political Behavior, Public Opinion, Political Psychology, Data Visualization

Joshua Tasoff

Associate Professor of Economic Sciences

Behavioral Economics, Experimental Economics, Animal Welfare Economics

DEDA consists of 64 units. You can complete your degree within 28 months, incorporating 5 semesters and 3 pre-semester summer intensives. Your coursework will include core courses, advanced topics, interdisciplinary courses, and a capstone project.

Transdisciplinary Core Courses (6 units) A unique feature of CGU professional doctorate programs is a shared Transdisciplinary core curriculum created to prepare business leaders. You will learn and collaborate with a diverse cohort of industry professionals on applied projects.

Students can choose 3 of the following courses:

• Leading Change and Innovation (2 units)
• Transdisciplinary Collaboration and Communication (2 units)
• Action in Crisis (2 units)
• Stakeholder Problem-solving (2 units)
• Systems-thinking (2 units)
• Diversity and Team Leadership (2 units)

Core Courses (16 units)

• Game Theory and Asymmetric Information (4 units)

Field course (select 3 courses, 4 units each) from one of the following fields:

• International and Economic Development Policy
• Applied Micro: Behavioral and Experimental Economics
• Applied Micro: Crime and Law Economics

Data Analysis (24 units)

• Computational Tools for Social Sciences (4 units)
• Econometrics I (4 units)
• Econometrics II (4 units)
• Data Visualization (4 units)
• Causal Modeling, Machine Learning & Big Data (4 units) OR Math of Machine Learning (4 units)
• Time Series Analysis (4 units)

Practicum (10 units)

• Practicum: Foundation (2 units)
• Practicum: Intermediate & Advanced Skills (2 units each, taken two times)
• Practicum: Integration and Application (2 units each, taken two times)

Capstone (8 units)

• Capstone: Proposal (2 units)
• Capstone: Progress (2 units)
• Capstone: Completion (4 units)

Your work will be assessed through course exams, research papers, presentations, group projects, practicum, and a capstone project. DEDA does not require a dissertation.

Learning Outcomes

The DEDA program equips you to become a translational researcher, capable of thinking and performing in an integrated, interdisciplinary manner. It prepares you to step into the role of an innovative investigator, forging new paths in your field. Upon successful completion of the DEDA program, you will be able to:

• Articulate the key theories, principles, and concepts in economics, and critically assess their practical value and application
• Demonstrate fluency in statistical techniques, as well as machine learning techniques, for applied economics
• Analyze and interpret complex data sets using econometric and machine learning algorithms, including crawling online content and defeating data hurdles
• Develop and submit grant proposals, create pre-analysis plans, and generate grant reports/interim reports for research projects
• Write reports on technical topics for a non-technical audience, effectively communicating complex concepts in economics and data analytics, and collaborate with practitioners, policymakers, and other stakeholders to ensure practical relevance and applicability
• Exhibit leadership skills in translational research, including the creation and use of new curricular elements and approaches, critical examination of research processes and methods, and leadership roles in a multidisciplinary collaboration aimed at problem-solving

Who Should Apply

Our DEDA degree was designed for working professionals who hold a master’s degree in economics or a related field and want to advance their careers outside of academia and research positions. In some cases, high-quality students with a BA/BS in economics or a related field, and at least 3 years of professional work experience, may be admitted. Your cohort may include students who hold jobs such as:

• Budget Analyst
• Consumer Behavior Scientist
• Data Scientist
• Investment Banker
• Statistician
• Vendor Manager

Application Guidelines

Key Dates & Deadlines

CGU operates on a priority deadline cycle. Applicants are strongly encouraged to submit complete applications by the priority dates in order to assure maximum consideration for both admission and fellowships.

Once the priority deadlines have passed, the University will continue to review applications for qualified candidates on a competitive, space-available basis. The final deadlines listed are the last date the University can accept an application in order to allow sufficient time to complete the admissions, financial aid, and other enrollment processes.

Spring 2025 Priority Deadline – November 1, 2024 Final Deadline (International) – November 15, 2024 Final Deadline (Domestic) – December 1, 2024 Classes begin – January 21, 2025

Summer 2025 Priority Deadline – February 1, 2025 Final Deadline (International) – March 1, 2025 Final Deadline (Domestic) – April 1, 2025 Classes begin – May 19, 2025

Fall 2025 Priority Deadline – February 1, 2025 Final Deadline (International) – July 5, 2025 Final Deadline (Domestic) – August 1, 2025 Classes begin – August 25, 2025

ESTIMATED TUITION (CALIFORNIA RESIDENTS, NON-RESIDENTS, INTERNATIONAL)

*Based on 2024-2025 tuition rates.

STUDENT FEES (PER SEMESTER)

For estimates of room & board, books, etc., please download CGU’s  Cost of Attendance 2024-2025 .

Review General Costs

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Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs immerse students in various cutting-edge research opportunities, where they develop innovative data analysis methods to tackle complex interdisciplinary problems and advance the underlying theoretical foundations driving those methods. .

Our Ph.D. programs in Statistics offer a dynamic blend of theoretical rigor and real-world application, equipping students with the skills to lead in academia, industry, and government.

Through immersive coursework, hands-on research, and collaboration with esteemed faculty, our students develop innovative statistical methodologies that push the boundaries of the field. Most students complete the program within 4-5 years, during which they build a strong foundation in both traditional and cutting-edge statistical practices. Our graduates leave our program prepared to tackle complex challenges and drive meaningful advancements in their chosen careers.

Have questions about the Ph.D. program?

Contact us at [email protected] .

What sets our Ph.D. experience apart?

Innovative joint ph.d. programs.

Our four joint Ph.D. programs invite students to dive into diverse fields such as machine learning , public policy , neuroscience , and the dynamic intersection of engineering and policy —equipping students to lead in today’s most transformative interdisciplinary areas of research.

Collaborate with World Renowned Machine Learning Department

CMU is a trailblazer in  Machine Learning  and Computer Science. Through our joint Ph.D. program in Statistics and Machine Learning, students gain exclusive access to world-class ML research and renowned faculty, many of whom hold joint appointments in both fields. This unique blend of expertise offers an unparalleled opportunity to be at the forefront of cutting-edge advancements in Statistics and Machine Learning.

Interdisciplinary Research Opportunities

Our faculty are deeply engaged in pivotal, data-rich scientific collaborations across diverse fields such as genetics, physical sciences, neuroscience, astronomy, and the social sciences. This opens doors for students to explore pressing questions and access the data essential for uncovering meaningful insights.

Immersive Research Experience

In their second semester, students start working on their Advanced Data Analysis Project - a distinctive, year-long collaboration with faculty that goes beyond traditional thesis work. This immersive experience offers an immediate opportunity to dive into intensive research, fostering hands-on skills and innovative thinking.

Application Process

Program requirements, post-graduation destinations.

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• Health Sciences and Technology (HST)
• Linguistics and Philosophy (Course 24)
• Management (Course 15)
• Media Arts and Sciences (MAS)
• Military Science (MS)
• Music (Course 21M)
• Naval Science (NS)
• Science, Technology, and Society (STS)
• Special Programs
• Supply Chain Management (SCM)
• Theater Arts (21T)
• Urban Studies and Planning (Course 11)
• Women's and Gender Studies (WGS)

Master of Engineering in Computer Science, Economics, and Data Science (Course 6-14P)

The Department of Electrical Engineering and Computer Science and the Department of Economics offer a joint curriculum leading to a Master of Engineering in Computer Science, Economics, and Data Science . Computer science and data science provide tools for problem solving, and economics applies those tools to domains where there is rapidly growing intellectual, scholarly, and commercial interest, such as online markets, crowdsourcing platforms, spectrum auctions, financial platforms, crypto currencies, and large-scale matching/allocation systems such as kidney donation and public school choice systems. This joint program prepares students for jobs in economics, management consulting, and finance. Students in the program are full members of both departments, with a single advisor chosen from EECS or Economics based on interests of the student as well as the advisor's interest and expertise in the 6-14 area.

The Master's of Engineering in Computer Science, Economics, and Data Science (Course 6-14P) builds on the foundation provided by the Bachelor of Science in Computer Science, Economics, and Data Science (Course 6-14) to provide both advanced classwork and master's-level thesis work. The student selects (with departmental review and approval) 42 units of advanced graduate subjects, which include two subjects in economics and two subjects in electrical engineering and computer science. A further 24 units of electives are chosen from a restricted departmental list of math electives.

The Master of Engineering degree also requires 24 units of thesis credit. While a student may register for more than this number of thesis units, only 24 units count toward the degree requirement.

Programs leading to the five-year Master of Engineering degree or to the four-year Bachelor of Science degree can be arranged to be identical through the junior year. At the end of the junior year, students with a strong academic record will be offered the opportunity to continue through the five-year master's program. A student in the Master of Engineering program must be registered as a graduate student for at least one regular (non-summer) term. To remain in the program and to receive the Master of Engineering degree, students will be expected to maintain a strong academic record. Admission to the Master of Engineering program is open only to undergraduate students who have completed their junior year in the Course 6-14 Bachelor of Science program.

Financial Support

The fifth year of study toward the Master of Engineering degree can be supported by a combination of personal funds, a fellowship, or a graduate assistantship. Assistantships require participation in research or teaching in the department or in one of the associated laboratories. Full-time assistants may register for no more than two scheduled classroom or laboratory subjects during the term, but may receive academic credit for their participation in the teaching or research program. Support through an assistantship may extend the period required to complete the Master of Engineering program by an additional term or two. Support is granted competitively to graduate students and will not be available for all of those admitted to the Master of Engineering program. If provided, department support for Master of Engineering candidates is normally limited to the first three terms as a graduate student unless the Master of Engineering thesis has been completed, the student has served as a teaching assistant, or the student has been admitted to the doctoral program, in which cases a fourth term of support may be permitted.

For additional information regarding teaching and research programs, contact the EECS Undergraduate Office, Room 38-476, 617-253-4654, or visit the department's website .

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PhD Program

Year after year, our top-ranked PhD program sets the standard for graduate economics training across the country. Graduate students work closely with our world-class faculty to develop their own research and prepare to make impactful contributions to the field.

Our doctoral program enrolls 20-24 full-time students each year and students complete their degree in five to six years. Students undertake core coursework in microeconomic theory, macroeconomics, and econometrics, and are expected to complete two major and two minor fields in economics. Beyond the classroom, doctoral students work in close collaboration with faculty to develop their research capabilities, gaining hands-on experience in both theoretical and empirical projects.

How to apply

Students are admitted to the program once per year for entry in the fall. The online application opens on September 15 and closes on December 15.

Meet our students

Our PhD graduates go on to teach in leading economics departments, business schools, and schools of public policy, or pursue influential careers with organizations and businesses around the world.