phd statistics and econometrics

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PhD in Econometrics and Statistics

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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

Chaoxing Dai

Wenxuan Guo

Shunzhuang Huang So Won (Sowon) Jeong

Jizhou Liu Edoardo Marcelli

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 2023-2024 Guidebook!

Department of Statistics

Last update: 11/10/23

PhD Degree in Statistics

The Department of Statistics offers an exciting and recently revamped PhD program that involves students in cutting-edge interdisciplinary research in a wide variety of fields. Statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence and machine learning. The massive increase in the data acquired, through scientific measurement on one hand and through web-based collection on the other, makes the development of statistical analysis and prediction methodologies more relevant than ever.

Our graduate program prepares students to address these issues through rigorous training in scientific computation, and in the theory, methodology, and applications of statistics. The course work includes four core sequences:

• Probability (STAT 30400, 38100, 38300)
• Mathematical statistics (STAT 30400, 30100, 30210)
• Applied statistics (STAT 34300, 34700, 34800)
• Computational mathematics and machine learning (STAT 30900, 31015/31020, 37710).

All students must take the Applied Statistics and Theoretical Statistics sequence. In addition it is highly recommended that students take a third core sequence based on their interests and in consultation with the Department Graduate Advisor (DGA). At the start of their second year, the students take two preliminary examinations. All students must take the Applied Statistics Prelim. For the second the students can choose to take either the Theoretical Statistics or the Probability prelim. Students planning to take the Probability prelim should take the Probability sequence as their third sequence.

Incoming first-year students have the option of taking any or all of these exams; if an incoming student passes one or more of these, then he/she will be excused from the requirement of taking the first-year courses in that subject. During the second and subsequent years, students can take more advanced courses, and perform research, with world-class faculty in a wide variety of research areas .

In recent years, a large majority of our students complete the PhD within four or five years of entering the program. Students who have significant graduate training before entering the program can (and do) obtain their doctor's degree in three years.

Most students receiving a doctorate proceed to faculty or postdoctoral appointments in research universities. A substantial number take positions in government or industry, such as in research groups in the government labs, in communications, in commercial pharmaceutical companies, and in banking/financial institutions. The department has an excellent track record in placing new PhDs.

Prerequisites for the Program

A student applying to the PhD program normally should have taken courses in advanced calculus, linear algebra, probability, and statistics. Additional courses in mathematics, especially a course in real analysis, will be helpful. Some facility with computer programming is expected. Students without background in all of these areas, however, should not be discouraged from applying, especially if they have a substantial background, through study or experience, in some area of science or other discipline involving quantitative reasoning and empirical investigation. Statistics is an empirical and interdisciplinary field, and a strong background in some area of potential application of statistics is a considerable asset. Indeed, a student's background in mathematics and in science or another quantitative discipline is more important than his or her background in statistics.

To obtain more information about applying, see the Guide For Applicants .

Students with questions may contact Yali Amit for PhD Studies, Mei Wang for Masters Studies, and Keisha Prowoznik for all other questions, Bahareh Lampert (Dean of Students in the Physical Sciences Division), or Amanda Young (Associate Director, Graduate Student Affairs) in UChicagoGRAD.

Handbook for PhD Students in Statistics

Information for first and second year phd students in statistics.

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Ph.d. program.

Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning. Life as a Ph.D. student in Statistical Science at Duke involves immersion in a broad range of research experiences and emphasizes conceptual innovation, as well as building a deep and broad foundation in theory and methods.

Coupled with our core emphases in modeling, computation and the methodologies of modern statistical science is a broad range of interdisciplinary relationships with many other disciplines (biomedical sciences, environmental sciences, genomics, computer science, engineering, finance, neuroscience, social sciences, and others). The rich opportunities for students in interdisciplinary statistical research at Duke are complemented by opportunities for engagement in research in summer projects with nonprofit agencies, industry, and academia.

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Department of Technology, Operations, and Statistics | Doctoral Program in Statistics

Doctoral program in statistics.

• Program of Study

Program Requirements

• Doctoral Students and Their Research
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Overview of the Doctoral Program in Statistics

The world’s financial markets produce an enormous stream of data, and the understanding of the techniques needed to analyze and extract information from this stream has become critical.   Doctoral work in statistics combines theory and methodology to deal with the large quantity of statistical data.  Here at Stern we use the theoretical and methodological orientation of a traditional statistics with a focus on the applications that are central to the concerns of a business school.  The PhD thesis work at Stern is a mathematically sophisticated enterprise that never loses sight of the real and practical problems of business.

Stern’s curriculum in statistics prepares students for academic positions by preparing them to conduct independent research.  The statistician must be knowledgeable of the basic issues of the intellectual areas in which his or her work will be applied. 

The most popular areas of student interest in the last few years have been mathematical finance, statistical modeling, data mining, stochastic processes, and econometrics.

Students have rigorous course work and participate in special topics seminars.  They work closely with the faculty and also present special PhD student seminars.

Clifford Hurvich Coordinator, Statistics Doctoral Program

Mission Our mission is the education of scholars who will produce first-rate statistics research and who will succeed as faculty members at first-rate universities.

Admissions and performance We enroll one or two students each year;  these are chosen from approximately 100 highly qualified applicants.

Advising and evaluation Each student will meet with a committee of faculty members yearly to assess progress through the program.

Research and interaction with faculty The Stern statistics faculty have a wide range of interests, but there is special emphasis on time series, statistical modeling, stochastic processes, and financial modeling.

PhD students in statistics take courses in statistical inference, stochastic processes, time series, regression analysis, and multivariate analysis.

In addition to course work, doctoral students also participate in research projects in conjunction with faculty members.  The students attend seminars, present seminars on their own work, and submit their work for publication.

The program culminates with the creation of the PhD thesis, through the stages of proposal, writing, and defense.

Most students finish in four to five years.

Statistics Program of Study

Statistics PhD students take their course work in the first two years of study.  These courses are taken within the Statistics Group (both as formal courses and also as independent study), within other departments at the Stern School, at NYU's Courant Institute, and at Columbia University.

In addition to their statistics courses, doctoral students in Statistics often take courses in mathematics, finance, market research, and econometrics.  The individual curriculum will be planned with the help of faculty advisers.

Questions about the PhD Program in Statistics?

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Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

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Interdisciplinary Doctoral Program in Statistics

• Minor in Statistics and Data Science
• MicroMasters program in Statistics and Data Science
• Data Science and Machine Learning: Making Data-Driven Decisions
• Norbert Wiener Fellowship
• Stochastics and Statistics Seminar
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• IDS.190 Topics in Bayesian Modeling and Computation
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• LIDS & Stats Tea

The Interdisciplinary PhD in Statistics (IDPS) is designed for students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st century statistics, using concepts of computation and data analysis as well as elements of classical statistics and probability within their chosen field of study.

Participating programs: Aeronautics & Astronautics Brain and Cognitive Sciences Economics Mathematics Mechanical Engineering Physics Political Science Social and Engineering Systems

How to join IDPS:

Doctoral students in participating programs may submit a selection form between the end of their second semester and penultimate semester in their doctoral program. Selection forms are due by the current semester add date, and students will be notified of a decision by the drop date.

Required documents include a CV, unofficial transcript, anticipated course plan and thesis proposal or statement of interest in statistics.  For access to the selection form or for further information, please contact the IDSS Academic Office at [email protected]

Graduate Departments:

MIT Statistics + Data Science Center Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 617-253-1764

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• Interdisciplinary PhD in Aero/Astro and Statistics
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• Interdisciplinary PhD in Economics and Statistics
• Interdisciplinary PhD in Mathematics and Statistics
• Interdisciplinary PhD in Mechanical Engineering and Statistics
• Interdisciplinary PhD in Physics and Statistics
• Interdisciplinary PhD in Political Science and Statistics
• Interdisciplinary PhD in Social & Engineering Systems and Statistics
• LIDS & Stats Tea
• Spring 2024
• Spring 2023
• Spring 2022
• Spring 2021
• Fall – Spring 2020
• Fall 2019 – IDS.190 – Topics in Bayesian Modeling and Computation
• Fall 2019 – Spring 2019
• Fall 2018 and earlier

PhD in Economics

The doctoral program in Economics at Rice University provides rigorous training in economic theory and econometrics in order to prepare students for research careers in economics. In 2014, the department launched the Rice Initiative for the Study of Economics (RISE) in order to enhance its role as a leading center of economic research. Since then, the department has hired ten new faculty. In addition, it has established itself as a leading institution for structural empirical microeconomics, an approach to economic analysis that combines economic theory and econometrics to address substantive economic issues. The small size of the program, approximately 45 graduate students working with 24 full-time faculty, promotes close faculty student interactions and collaboration, as well as strong relationships among the students.

Effective 2021-2022 academic year, all students receiving MA or PhD degrees in Economics will also receive Major Concentration in Econometrics and Quantitative Economics, and will be able to apply for a 24-month STEM extension of F-1 Optional Practical Training (OPT).

At a Glance

Full-time faculty working with students : 24

Students : 55 doctoral students

Number Admitted : 7-10 students each year

Fields of Study : Theoretical and Applied Econometrics, Applied Microeconomics, Economic Theory, Financial Economics, Game Theory, Political Economics, Energy Economics, Development Economics, Empirical Industrial Organization, Labor Economics, Macroeconomics, Public Finance, Health Economics.

Degrees awarded : PhD in Economics with a Major Concentration in Econometrics and Quantitative Economics (STEM designated) , Ph.D. in Economics with an M.A. in Statistics , and an additional Major Concentration in Finance . An M.A. degree is awarded to students pursuing Ph.D. in Statistics when they complete the requirements for M.A. in Economics with a Ph.D. in Statistics . It is also awarded to students who complete all the requirements for the Ph.D. in Economics working toward their dissertation.

Learn More about the Economics Doctorate Program

Monday, May. 9, 2022

Monday, Jan. 11, 2021

Friday, Jun. 5, 2020

Monday, Feb. 10, 2020

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Interdisciplinary Doctoral Program in Statistics

The Interdisciplinary Doctoral Program in Statistics is an opportunity for students in a multitude of disciplines to specialize at the doctoral level in a statistics-grounded view of their field. Participating programs include Aeronautics and Astronautics, Brain and Cognitive Sciences, Economics, Mathematics, Mechanical Engineering, Physics, Political Science, and the IDSS Social and Engineering Systems Doctoral Program.

The program is administered jointly by the Statistics and Data Science Center and the participating academic units. Students enrolled in a doctoral program in a participating department may choose to be considered for the Interdisciplinary Doctoral Program in Statistics. Please refer to the program's website for details on the selection process.

Selected students will complete the home department’s degree requirements (including the qualifying exam) along with specified statistics requirements including a doctoral seminar, coursework in probability, statistics, computation and statistics, and data analysis, and a dissertation that utilizes statistical methods in a substantial way. 

For more information about the program, contact the Statistics Academic Administrator .

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The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

Doctoral Program

The Ph.D. program is a full time program leading to a Doctoral Degree in Economics.  Students specialize in various fields within Economics by enrolling in field courses and attending field specific lunches and seminars.  Students gain economic breadth by taking additional distribution courses outside of their selected fields of interest.

General requirements

Students  are required to complete 1 quarter of teaching experience. Teaching experience includes teaching assistantships within the Economics department or another department .

University's residency requirement

135 units of full-tuition residency are required for PhD students. After that, a student should have completed all course work and must request Terminal Graduate Registration (TGR) status.

Department degree requirements and student checklist

1. core course requirement.

Required: Core Microeconomics (202-203-204) Core Macroeconomics (210-211-212) Econometrics (270-271-272).  The Business School graduate microeconomics class series may be substituted for the Econ Micro Core.  Students wishing to waive out of any of the first year core, based on previous coverage of at least 90% of the material,  must submit a waiver request to the DGS at least two weeks prior to the start of the quarter.  A separate waiver request must be submitted for each course you are requesting to waive.  The waiver request must include a transcript and a syllabus from the prior course(s) taken.  

2.  Field Requirements

Required:  Two of the Following Fields Chosen as Major Fields (click on link for specific field requirements).  Field sequences must be passed with an overall grade average of B or better.  Individual courses require a letter grade of B- or better to pass unless otherwise noted.

Research fields and field requirements :

• Behavioral & Experimental
• Development Economics
• Econometric Methods with Causal Inference
• Econometrics
• Economic History
• Environmental, Resource and Energy Economics
• Industrial Organization
• International Trade & Finance
• Labor Economics
• Market Design
• Microeconomic Theory
• Macroeconomics
• Political Economy
• Public Economics

3.  Distribution

Required:  Four other graduate-level courses must be completed. One of these must be from the area of economic history (unless that field has already been selected above). These courses must be distributed in such a way that at least two fields not selected above are represented.  Distribution courses must be passed with a grade of B or better.

4.  Field Seminars/Workshops

Required:  Three quarters of two different field seminars or six quarters of the same field seminar from the list below.   

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Economic Analysis & Policy

Our doctoral program in the field of economic analysis and policy prepares students for research careers in economics. The program offers rigorous training and has several distinct advantages:

Low Student-to-Faculty Ratio

First, enrollment in the program is small. This encourages close faculty-student contact and allows students to become involved in research very early. Students work first as assistants on faculty research projects and, as their interests and skills develop, on their own research. Students often begin their publishing careers before completing their degrees.

Flexible and Innovative Program

Second, the program is flexible and innovative; students can draw on both the school’s and the university’s distinguished faculty. In addition to the faculty in the economics group at Stanford GSB and in the university’s economics department, students have access to faculty in political and behavioral sciences; accounting and finance; mathematics, statistics, and computer science; and many other disciplines.

A Top-Ranked School

Third, the program is part of a top-ranked professional school. This setting allows students to gain a deeper understanding of the actual processes of business decision-making and public policy formulation.

Preparation and Qualifications

Students who enroll in this program have a substantial background in economics and mathematics. They are expected to have, minimally, mathematical skills at the level of one year of advanced calculus and one course each in linear algebra, analysis, probability, optimization, and statistics.

The faculty selects students based on predicted performance in the program. Evidence of substantial background or ability in the use of mathematical reasoning and statistical methods is important. Most successful applicants had quantitative undergraduate majors in economics, mathematics, or related sciences.

In addition to evidence of ability and letters of recommendation, the faculty considers carefully the applicant’s statement of purpose for pursuing the PhD degree. The successful applicant usually has clearly defined career goals that are compatible with those of the program.

Acceptance into the program is extremely competitive. Admitted applicants compare very favorably with students enrolled in the top economics departments of major universities.

Economic Analysis & Policy Faculty

Mohammad akbarpour, claudia allende santa cruz, susan athey, lanier benkard, jeremy i. bulow, modibo khane camara, sebastian di tella, rebecca diamond, yossi feinberg, guido w. imbens, charles i. jones, jonathan levin, michael ostrovsky, garth saloner, yuliy sannikov, kathryn shaw, andrzej skrzypacz, paulo somaini, takuo sugaya, juan carlos suárez serrato, christopher tonetti, shoshana vasserman, ali yurukoglu, weijie zhong, emeriti faculty, alain c. enthoven, robert j. flanagan, david m. kreps, peter c. reiss, john roberts, a. michael spence, robert wilson, recent publications in economic analysis & policy, trading stocks builds financial confidence and compresses the gender gap, drivers of public procurement prices: evidence from pharmaceutical markets, redistributive allocation mechanisms, recent insights by stanford business, a “grumpy economist” weighs in on inflation’s causes — and its cures, if/then: why research matters, at what point do we decide ai’s risks outweigh its promise, placement director.

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• Course schedules

Doctoral programme in Econometrics and Statistics

The curriculum consists of two phases: a first phase of intensive coursework and a second phase of advanced studies and research. The course phase is designed to provide students with sufficient methodological background to write a quantitatively oriented PhD Thesis on topics related to Economics or Finance. The first phase consists of 10 core courses with corresponding exams. Each course is awarded with 3 study credits, for a total of 30 ECTS. A part of the courses is mandatory (core courses) and another part consists of electives that can be taken at USI or other universities, e.g., in the ProDoc or the NCCR FINRISK programmes, conditional on a formal prior acceptance by the PhD committee. Examples for a list of possible potential topics for the electives in the curriculum is given below.

First year: Core Courses

• Probability Theory and Stochastic Processes (local faculty)
• Statistics (invited faculty or local faculty)
• Econometrics (local faculty)
• Selected Topics in Statistics (local faculty)
• Time Series Analysis (local faculty)

First year: Elective Courses

• Topics in Quantitative Economics and Finance (invited faculty or taken abroad)
• Topics in Econometrics (invited faculty or taken abroad)
• Financial Econometrics (invited faculty or taken abroad)
• Optimization Methods for Economics (taken abroad)
• Bayesian methodology and Markov chain Monte Carlo algorithms (local faculty)

Teaching is in English. Upon admission to the second phase of the programme, doctoral students select a thesis topic and find their thesis advisor. The completion of the PhD programme requires a final defence of the PhD thesis.

For further information please contact Prof. Paul Schneider email   [email protected]

Admissions are currently closed. Please check this website for openings.

• Directives for the PhD in Econometrics and Statistics / Direttive per il Dottorato in Scienze economiche con specializzazione in Metodi Quantitativi (pdf)
• Regulations for the Degree of PhD, 17.09.2015 (pdf)
• Enrollment Form to the Doctoral School (pdf)
• PhD in Economics
• PhD in Finance
• PhD in Management
• PhD at the Faculty of Economics: overview
• Current openings
• How to get to the Faculty

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MPhil/PhD Statistics

• Graduate research
• Department of Statistics
• Application code G4ZS
• Starting 2024
• Home full-time: Closed
• Overseas full-time: Closed
• Location: Houghton Street, London

Programme details

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Entry requirements

Minimum entry requirements for mphil/phd statistics.

The minimum entry requirement for this programme is a distinction in a taught master’s (or equivalent) with substantial statistical content, or equivalent experience.  

Competition for places at the School is high. This means that even if you meet our minimum entry requirement, this does not guarantee you an offer of admission. 

If you have studied or are studying outside of the UK then have a look at our  Information for International Students  to find out the entry requirements that apply to you.

Assessing your application

We welcome applications for research programmes that complement the academic interests of members of staff at the School, and we recommend that you investigate  staff research interests  before applying.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including existing and pending qualifications) - statement of academic purpose - references - CV - research proposal - sample of written work.

See further information on supporting documents

You may also have to provide evidence of your English proficiency. You do not need to provide this at the time of your application to LSE, but we recommend that you do.  See our English language requirements .

Most applicants will have little or no prior experience of research and therefore we do not expect a fully-developed research proposal. We are assessing the potential of the applicant for research and the chosen topic. The following is a guideline of what to emphasise in the proposal.

• pose a research question rather than a very broad research topic
• be specific, to aid selectors to assess the suitability of the topic for PhD study
• a statement of how the proposed research builds on earlier research on the topic, with reference to two or three key papers
• demonstrate your understanding of the area and the need for further research
• selectors will look for a sense of the merits of your approach
• most topics will involve an application of the proposed methods to a substantive research question. Give a brief outline of this    question and explain how it will benefit from this particular approach
• be specific about the training and skills you have to undertake the proposed research (do not simply list courses attended: this information is already available in the CV and transcripts). 

When to apply

The application deadline for this programme is 23 May 2024 . However, to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details.

Fees and funding

Every research student is charged a fee in line with the fee structure for their programme. The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover  living costs  or travel or fieldwork.

Tuition fees 2024/25 for MPhil/PhD Statistics

Home students: £4,786 for the first year Overseas students: £22,632 for the first year

The fee is likely to rise over subsequent years of the programme. The School charges home research students in line with the level of fee that the Research Councils recommend. The fees for overseas students are likely to rise in line with the assumed percentage increase in pay costs (ie, 4 per cent per annum).

The Table of Fees shows the latest tuition amounts for all programmes offered by the School.

Fee status​

The amount of tuition fees you will need to pay, and any financial support you are eligible for, will depend on whether you are classified as a home or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information about fee status classification.

Scholarships, studentships and other funding

The School recognises that the  cost of living in London  may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students.

This programme is eligible for  LSE PhD Studentships , and  Economic and Social Research Council (ESRC) funding . Selection for the PhD Studentships and ESRC funding is based on receipt of an application for a place – including all ancillary documents, before the funding deadline.  

Funding deadline for LSE PhD Studentships and ESRC funding: 15 January 2024

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas.  Find out more about financial support.

Statistics PhD Scholarship

The Department of Statistics offers one studentship to a 2023/24 offer holder covering fees and living expenses for four years. This scholarship is available for a home or overseas student undertaking research in any statistics discipline, with annual renewal subject to satisfactory academic performance. The scholarship is awarded strictly on academic merit and research potential. To be considered for this scholarship you must submit your application, including all supporting documentation, by 13 January 2023.

External funding 

There may be other funding opportunities available through other organisations or governments and we recommend you investigate these options as well.

Further information

Fees and funding opportunities

Information for international students

LSE is an international community, with over 140 nationalities represented amongst its student body. We celebrate this diversity through everything we do.  

If you are applying to LSE from outside of the UK then take a look at our Information for International students . 

1) Take a note of the UK qualifications we require for your programme of interest (found in the ‘Entry requirements’ section of this page). 

2) Go to the International Students section of our website. 

3) Select your country. 

4) Select ‘Graduate entry requirements’ and scroll until you arrive at the information about your local/national qualification. Compare the stated UK entry requirements listed on this page with the local/national entry requirement listed on your country specific page.

Programme structure and courses

In addition to progressing with your research, you are expected to take the listed training and transferable skills courses. You may take courses in addition to those listed but you must discuss this with your supervisor.

At the end of your second year (full-time), you will need to satisfy certain requirements and if you meet these, will be retroactively upgraded to PhD status.

(* denotes half unit)

Training courses

Compulsory (examined)

Probability and Mathematical Statistics I*

Statistical Modelling and Data Analysis*

And one of:

Foundations of Machine Learning*

Probability and Mathematical Statistics II* Students may take a different course option with the agreement of both the supervisor and PhD Programme Director.

Optional (examined)

Courses offered by the  London Graduate School in Mathematical Finance Courses offered by the  London Taught Course Centre Optional (examined) Master's-level courses relevant to research and agreed by supervisor in Department, the School or University of London College

Transferable skills courses

Compulsory (not examined)

One presentation

Attendance of departmental seminar appropriate to the student's field of study.

Optional (not examined) London Graduate School in Mathematical Finance PhD Presentation Day

Poster Presentations The Department encourages you to attend and, where the opportunity arises, present a paper or poster at conferences during your PhD programme in relation to your particular research topic.

Optional (examined) Courses provided by the Department of Methodology 

Second year

Optional (not examined) Courses offered by the London Graduate School in Mathematical Finance Courses offered by the London Taught Course Centre Optional (examined) Master’s-level courses relevant to research and agreed by supervisor in Department, the School or University of London College

Two presentations 

Attendance of departmental seminars appropriate to the student's field of study.

Optional (not examined) London Graduate School in Mathematical Finance Seminar Day

Optional (not examined) Courses offered by the  London Graduate School in Mathematical Finance Courses offered by the  London Taught Course Centre Optional (examined) Master’s-level courses relevant to research and agreed by supervisor in the Department, the School or University of London College

Two presentations

Optional (not examined) London Graduate School in Mathematical Finance PhD Presentation Day Poster Presentations The Department encourages you to attend and, where the opportunity arises, present a paper or poster at conferences during your PhD programme in relation to your particular research topic.

Fourth year

Optional (not examined) Courses offered by the  London Graduate School in Mathematical Finance Courses offered by the  London Taught Course Centre

Optional (examined) Master's level courses relevant to research and agreed by supervisor in the Department, the School or University of London College

Optional (not examined)

London Graduate School in Mathematical Finance Seminar Day

Optional (examined) Courses provided by the Department of Methodology.

For the most up-to-date list of optional courses please visit the relevant School Calendar page. 

You must note, however, that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.  

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s  Calendar , or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the  updated graduate course and programme information page.

Supervision, progression and assessment

Supervision.

You will be assigned a lead supervisor (and a second supervisor/adviser) who is a specialist in your chosen research field, though not necessarily in your topic. Lead supervisors guide you through your studies.

You may wish to first send an informal application email to the Department of Statistics to enquire about making a formal application within the area of your research interests and to check about the availability of potential supervisors. 

Progression and assessment

Formal assessment is made towards the end of each Spring Term. This assessment is based on statements made by you and the supervisors in the progress report form. You are also required to complete a supplementary report of one to two pages (A4), providing in more detail an outline of your current research.

The review to upgrade to the PhD normally takes place within two years of full-time registration. Progress is assessed by the first and/or second supervisor in consultation with the PhD programme director and another expert in the field of the research you are undertaking. If satisfactory progress has been made, the programme director will recommend that registration be upgraded to PhD status. The Department's research committee also monitors the progress of PhD students.

Student support and resources

We’re here to help and support you throughout your time at LSE, whether you need help with your academic studies, support with your welfare and wellbeing or simply to develop on a personal and professional level.

Whatever your query, big or small, there are a range of people you can speak to who will be happy to help.  

Department librarians   – they will be able to help you navigate the library and maximise its resources during your studies. 

Accommodation service  – they can offer advice on living in halls and offer guidance on private accommodation related queries.

Class teachers and seminar leaders  – they will be able to assist with queries relating to specific courses. 

Disability and Wellbeing Service  – they are experts in long-term health conditions, sensory impairments, mental health and specific learning difficulties. They offer confidential and free services such as  student counselling,  a  peer support scheme  and arranging  exam adjustments.  They run groups and workshops.  

IT help  – support is available 24 hours a day to assist with all your technology queries.   

LSE Faith Centre  – this is home to LSE's diverse religious activities and transformational interfaith leadership programmes, as well as a space for worship, prayer and quiet reflection. It includes Islamic prayer rooms and a main space for worship. It is also a space for wellbeing classes on campus and is open to all students and staff from all faiths and none.   

Language Centre  – the Centre specialises in offering language courses targeted to the needs of students and practitioners in the social sciences. We offer pre-course English for Academic Purposes programmes; English language support during your studies; modern language courses in nine languages; proofreading, translation and document authentication; and language learning community activities.

LSE Careers  ­ – with the help of LSE Careers, you can make the most of the opportunities that London has to offer. Whatever your career plans, LSE Careers will work with you, connecting you to opportunities and experiences from internships and volunteering to networking events and employer and alumni insights. 

LSE Library   –   founded in 1896, the British Library of Political and Economic Science is the major international library of the social sciences. It stays open late, has lots of excellent resources and is a great place to study. As an LSE student, you’ll have access to a number of other academic libraries in Greater London and nationwide. 

LSE LIFE  – this is where you should go to develop skills you’ll use as a student and beyond. The centre runs talks and workshops on skills you’ll find useful in the classroom; offers one-to-one sessions with study advisers who can help you with reading, making notes, writing, research and exam revision; and provides drop-in sessions for academic and personal support. (See ‘Teaching and assessment’). 

LSE Students’ Union (LSESU)  – they offer academic, personal and financial advice and funding.  

PhD Academy   – this is available for PhD students, wherever they are, to take part in interdisciplinary events and other professional development activities and access all the services related to their registration. 

Sardinia House Dental Practice   – this   offers discounted private dental services to LSE students.  

St Philips Medical Centre  – based in Pethwick-Lawrence House, the Centre provides NHS Primary Care services to registered patients.

Student Services Centre  – our staff here can answer general queries and can point you in the direction of other LSE services.  

Student advisers   – we have a  Deputy Head of Student Services (Advice and Policy)  and an  Adviser to Women Students  who can help with academic and pastoral matters.

Student life

As a student at LSE you’ll be based at our central London campus. Find out what our campus and London have to offer you on academic, social and career perspective. 

Student societies and activities

Your time at LSE is not just about studying, there are plenty of ways to get involved in  extracurricular activities . From joining one of over 200 societies, or starting your own society, to volunteering for a local charity, or attending a public lecture by a world-leading figure, there is a lot to choose from. 

The campus 

LSE is based on one  campus  in the centre of London. Despite the busy feel of the surrounding area, many of the streets around campus are pedestrianised, meaning the campus feels like a real community. 

Life in London 

London is an exciting, vibrant and colourful city. It's also an academic city, with more than 400,000 university students. Whatever your interests or appetite you will find something to suit your palate and pocket in this truly international capital. Make the most of career opportunities and social activities, theatre, museums, music and more. 

Want to find out more? Read why we think  London is a fantastic student city , find out about  key sights, places and experiences for new Londoners . Don't fear, London doesn't have to be super expensive: hear about  London on a budget . 

Student stories

MPhil/PhD Statistics Suzhou, China

Statistics is important in every industry in modern society; you need statistics to analyse data and ultimately to solve empirical problems. My programme enables me to apply my statistical knowledge to real world problems in finance and economics. 

Quick Careers Facts for the Department of Statistics

Median salary of our PG students 15 months after graduating: £38,000

Top 5 sectors our students work in:

• Financial and Professional Services              
• Information, Digital Technology and Data            
• FMCG, Manufacturing and Retail              
• Accounting and Auditing              
• Government, Public Sector and Policy

The data was collected as part of the Graduate Outcomes survey, which is administered by the Higher Education Statistics Agency (HESA). Graduates from 2020-21 were the fourth group to be asked to respond to Graduate Outcomes. Median salaries are calculated for respondents who are paid in UK pounds sterling and who were working in full-time employment.

Students who successfully complete the programme often embark on an academic career. Recent doctoral graduates have also gone into careers in investment banking.  See career destinations for some of our former students .

Further information on graduate destinations for this programme

Support for your career

Many leading organisations give careers presentations at the School during the year, and LSE Careers has a wide range of resources available to assist students in their job search. Find out more about the  support available to students through LSE Careers .

Find out more about LSE

Discover more about being an LSE student - meet us in a city near you, visit our campus or experience LSE from home. 

Experience LSE from home

Webinars, videos, student blogs and student video diaries will help you gain an insight into what it's like to study at LSE for those that aren't able to make it to our campus.  Experience LSE from home . 

Come on a guided campus tour, attend an undergraduate open day, drop into our office or go on a self-guided tour.  Find out about opportunities to visit LSE . 

LSE visits you

Student Marketing, Recruitment and Study Abroad travels throughout the UK and around the world to meet with prospective students. We visit schools, attend education fairs and also hold Destination LSE events: pre-departure events for offer holders.  Find details on LSE's upcoming visits . 

How to apply

Virtual Graduate Open Day

Register your interest

Related programmes, mphil/phd mathematics.

Code(s) G1ZM

MSc Statistics (Research)

Code(s) G4U1

MSc Statistics (Financial Statistics) (Research)

Code(s) G4U7

MSc Data Science

Code(s) G3U1

MSc Statistics (Social Statistics) (Research)

Code(s) G3U3

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• Dissertation Areas and Joint PhD Programs
• PhD Career Outcomes
• PhD Proposals and Defenses
• PhD Job Market Candidates
• PhD Research Community
• 100 Years of Pioneering Research
• Rising Scholars Conference
• Yiran Fan Memorial Conference
• Frequently Asked Questions
• PhD in Accounting
• PhD in Behavioral Science

PhD in Econometrics and Statistics

• PhD in Economics
• PhD in Finance
• PhD in Management Science and Operations Management
• PhD in Marketing
• Joint Program in Financial Economics
• Joint Program in Psychology and Business
• Joint PhD/JD Program

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

Chaoxing Dai

Wenxuan Guo

Shunzhuang Huang So Won (Sowon) Jeong

Jizhou Liu Edoardo Marcelli

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 2023-2024 Guidebook!

Department of Economics, Management and Statistics  DEMS

Browse the section

• Research centres

PhD in Economics, Statistics and Data Science

• PhD in Strategic Innovation for Sustainable and Smart Ecosystems (SIS2E)
• Fatti e persone
• Working papers

The four-year PhD in Economics, Statistics and Data Science (ECOSTATDATA)  provides the most effective response to the important challenges which nowadays doctoral programmes in the areas of economics, statistics and data analytics, both in Italy and Europe, have to cope with: i) high qualification of the faculty, in terms of teaching abilities and publication records; ii) capability of attracting high quality students; iii) interdisciplinarity; iv) internationalization; v) relations with the non-academic job market; vi) placement of students who have successfully discussed their dissertations.

ECOSTATDATA builds upon the fruitful collaboration among economists, statisticians and data scientists from the Department of Economics, Management and Statistics (DEMS) and the Department of Statistics and Quantitative Methods of the University of Milano-Bicocca (UniMiB), which has started twenty years ago within the BSc in Statistics and Economics, as well as the MSc in Statistics and Economics and is going on with the more recent MSc in Data Science.

Coordinator : Prof.  Matteo Manera

Deputy Coordinator : Prof.  Giorgio Vittadini

Administration Office: Mrs. Clara Sereni

NEW!!! Call for applications 2024-2025 (XL cycle)

A.A. 2024-2025 (cycle XL)

Call for Applications

DEMS - University of Milano-Bicocca, Italy

The Department of Economics, Management and Statistics (DEMS) of the University of Milano-Bicocca invites applications to its PhD Programme in Economics, Statistics and Data Science (ECOSTATDATA) for the academic year 2024-25 (XL cycle).

The PhD Programme is articulated in  three curricula , Economics (ECO), Statistics (STAT) and Big Data & Analytics for Business (BIDAB). The length of the PhD Programme is  four years , starting in late October 2024 (the precise starting date will be announced in due course on the PhD website).

The Call of Applications 2024-2025 offers at least 10 fully-funded scholarships .

The selection procedure is regulated by the official Call for Applications (Bando di Concorso), which will be published in the Doctoral School’s and in the PhD programme websites  on April 12, 2024 , with deadline on May 14, 2024.

The official Call for Applications contains detailed information on: i) the documents which each candidate has to submit; ii) structure, contents and timing (May 27, 2024 - June 21, 2024) of the entrance examination; iii) description of the projects related to the scholarships and positions offered.

The official Call for Applications will be published  here .

The PhD programme (in a nutshell...)

Introduction.

ECOSTATDATA belongs to the PhD School of UniMiB, it is affiliated to DEMS, it lasts four years and it is articulated in three curricula, the original two curricula Economics (ECO) and Statistics (STAT), and, starting from cycle XXXVII (academic year 2021-2022), the “new” curriculum Big Data & Analytics for Business (BiDAB) .

The first-year teaching activities are mainly devoted to structured courses (tool courses), which are compulsory. Some of these courses are fixed and specific to each curriculum, some are in common between the three curricula, some other courses are chosen by students within each curriculum.

The second-year teaching activities take the form of less structured courses (elective courses or reading groups).

In general, the first-year courses are offered by “internal” teachers, while second-year courses are often open to the collaboration of foreign instructors (visiting scholars).

The curriculum Economics (ECO)

This curriculum is indicated to students with a strong background in quantitative economics and provides advanced training in econometrics, microeconometrics, time series analysis, microeconomics and macroeconomics.

The curriculum Statistics (STAT)

This curriculum is designed for students with a strong background in statistics, both methodological and applied , and provides advanced training in probability, stochastic processes, statistical inference, Bayesian statistics, statistical learning, statistical modelling, computational statistics and data analysis.

The “new” curriculum Big Data & Analytics for Business (BiDAB)

This curriculum starts from cycle XXXVII (academic year 2021-2022) , and provides students with rigorous training in data management and programming, with focus on: the analysis of large amounts of structured and unstructured data (natural language); the main paradigms of big data and data visualization, based on the use of innovative techniques of machine learning, text and web mining.

“Flexible” and “training” profiles

By means of appropriate sequences of courses, suggested and monitored by the Programme Committee and the supervisors, students are able to build up “flexible” profiles, which are mainly addressed to scientific research, both in universities or in non-academic institutions, at national or international level.

ECOSTATDATA facilitates the interaction between economic, statistical and data management skills by proposing innovative “training” profiles, which are  mainly addressed to the non-academic job market. The “training” profiles aim at:

• offering to the non-academic job market high-level skills which are not currently available;
• attracting students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented;
• eliciting the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of a PhD scholarship on specific research projects.

Length of the programme

The current length of many PhD programmes in economics, statistics and data science in Italy, including the PhD in Economics DEFAP-Bicocca and in Statistics and Mathematical Finance of UniMiB, is three years. This length is insufficient to guarantee that the PhD theses meet the quality standards achieved by the best European PhD programmes. For this reason, ECOSTATDATA lasts four years . This duration is in line with the recent choices of some of the best Italian PhD programmes in economics, statistics and data science, as well as the PhD programmes in this area offered by the most prestigious European academic institutions.

Interdisciplinarity

ECOSTATDATA fosters interdisciplinary research activities, by favouring co-tutorships between economists, statisticians and data scientists, as well as through the “flexible” and “training” profiles.

Relations with the non-academic job market

ECOSTATDATA is particularly active in collaborating with national, multi-national, high-quality and innovation-oriented companies. In particular, ECOSTATDATA is able to: i) offer high-level skills which are not currently available on the non-academic job market; ii) attract students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented; iii) elicit the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the modern instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of PhD scholarships on specific research projects.

Internationalization

The international experience which has flourished within the PhD in Economics DEFAP-Bicocca and the PhD in Statistics and Mathematical Finance of UniMiB, together with the professional networks developed by many faculty members, guarantees that ECOSTATDATA is particularly active in collaborating with prestigious foreign universities, in terms of both students and faculty members exchange programs and joint degrees.

ECOSTATDATA is managed by two bodies:

• the Programme Committee (PC), that is the executive and decision-making board composed by full professors, associate professors and researcher of UniMiB and from other renowned Italian and foreign universities and research institutions;
• the Advisory Board (AB), which collaborates with the PC to organize the teaching and research activities of the programme, is headed by the programme Coordinator and is formed by a limited number of professors and researchers who are representative of the three curricula.

Teaching activities

The teaching activities proposed by ECOSTATDATA are organized during the first two years and differ for each curriculum, although some courses are common. Some economics courses at the first and the second year within the curriculum Economics can be offered jointly with the PhD programme in Economics and Finance of the Catholic University of Milano.

First- year courses

• Curriculum Economics (selected courses)

Mathematics; Computational Statistics I; Econometrics; Microeconometrics; Time Series Analysis; Microeconomics; Macroeconomics; Research Methods; Finance.

• Curriculum Statistics (selected courses)

Mathematical Analysis, Numerical Optimization, Probability, Stochastic Processes, Bayesian Statistics, Statistical Inference, Statistical Learning, Computational statistics II, Statistical Modelling, R for Data Science, Data Management.

• Curriculum Big Data & Analytics for Business (selected courses)

Databases for Structured/Unstructured Data (SQL); Programming in Python; Data Quality and Cleaning for Big Data; Architecture for Big Data Processing; Machine Learning; Cloud & Distributed Algorithm; Data Mining; Natural Language processing and Understanding; Human-Centered AI; Social Media Analysis; Semantic Web; Deep Learning and Computer Vision for Business; Data Visualization & Visual Analysis.

Second-year courses

Second-year courses are mainly “reading groups”, that are built upon the research interests of both instructors and students, and are  articulated into one/two introductory lecture/s and a series of meetings where students critically discuss the readings assigned by the instructor during the initial lecture.

The second-year courses are generally offered during the first part of the second year, in order forstudents to be full-time dedicated to their dissertations as early as possible.

Within each curriculum, a careful selection of courses, monitored by the PC and the student’s supervisor, allows each student to identify a “flexible” profile, which coherent with his/her research interests.

Generally, structured courses have written exams, while the exams associated with the reading groups are more flexible (e.g. written projects and/or oral presentations). The organization of the exams (i.e. form, number of questions, etc.) is decided by the PC and communicated to students at the beginning of each course. 

Monitoring the quality of teaching

The PC runs every year a systematic evaluation of the quality of the courses offered by the PhD programme, by submitting to each student of a given course a detailed questionnaire. Data from the questionnaires are elaborated statistically, sent to each instructor, and discussed within the PC, in order to identify potential problems and solutions.

Admissions to the second year and to years after the second

Admission to the second year is based on the performance of each student in the first-year exams, including the number of “fail” and the number of “resits” each student has been given. Admissions to the third and the fourth years are based on the progresses of the research work. Rules on admission to the second and subsequent years, as well as all the other rules regulating the teaching and research activities of ECOSTAT are formalized by the PC and communicated to each student after enrollment.

Research activities

The Programme Committee (PC) approves the (minimum) number of papers which form a typical PhD dissertation, namely 2. These papers have to be self-contained, independent and potentially publishable on high-quality internationally refereed journals.

Supervision

In order to facilitate students in identifying a sound research project and a suitable supervisor, within the first part of the year the PC organizes a presentation of the research groups which are active among the PC and the Advisory Board (AB) members. Supervisors are asked to systematically monitor the progresses made by their supervisees and periodically report to the PC about the proceedings of their dissertations.

PhD students, especially from the second year, are strongly invited to attend the department seminars organized on a weekly basis at UniMiB. Students of both curricula are also invited to present the progress of their research work in specific seminars, which are part of the student’s evaluation process and, if possible, are jointly organized in order to enhance cross-fertilization between economists, statisticians and data scientists. 

Admission to third and fourth year

Admission to the third and fourth year is formalized by the PC, based on the evaluation of the student’s research work. Admission to the third year takes also into account the performance of each student in the second-year exams.

Admission to external evaluation

Fourth-year students should present, by the end of the year, the final version of their dissertation in front of the PC. If possible, each presentation will be assigned a discussant. The admission to the external reviewers is formalized by the PC, based on the overall evaluation of the PhD thesis.

Thesis discussion

Based on the reports of the external reviewers, students are admitted to the discussion in front of the Evaluation Committee either with minor or major revisions. Students who have successfully defended their dissertation are awarded by the Evaluation Committee the title of “PhD in Economics and Statistics” (students enrolled in cycles XXXIV, XXXV and XXXVI) or the title of “PhD in Economics, Statistics and Data Science” (students enrolled from cycle XXXVII). Students can request to (and obtain from) the Administrative Offices of UniMiB an official document reporting the specific curriculum they have been enrolled in.

ECOSTATDATA takes care of the optimal placement of its students. On this respect, the Programme Committee is very active in: i) providing students with systematic and detailed information on the job market, domestic and international, academic and non-academic; ii) advising and assisting students who intend to apply for academic positions abroad.

Programme committee

Research groups

The research activities which characterize the PhD programme in Economics, Statistics and Data Science (ECOSTATDATA) are carried out by an active and lively community of junior and senior researchers.

Within DEMS, researchers are organized in clusters , among which the most relevant for ECOSTATDATA are:

- Business, economic and social statistics (coordinator: Prof. Pelagatti)

- Empirical microeconomics and microeconometrics (coordinator: Prof. Manera)

- Experimental and behavioural economics (coordinator: Prof. Stanca)

- Macroeconomics and macroeconometrics (coordinator: Prof. Morana)

- Microeconomics: theory and applications (coordinator: Prof. Gilli)

- Statistics (coordinator: Prof. Ongaro)

- Strategy, organization and innovation (coordinator: Prof. Torrisi)

Detailed information about people involved in each cluster can be found here .

The other two main groups of researchers supporting the programme are affiliated to the Department of Statistics and Quantitative Methods (DiSMeQ) of UniMiB and to the Department of Statistics (DiSTAT), Catholic University of Milano.

Detailed information about the research activities carried on by the DiSMeQ members can be found here .

Detailed information about the research activities carried on by the DiSTAT members can be found here .

Ex-alumni - XXXIV cycle

Supervisor(s): Prof. Silvia Biffignandi , University of Bergamo

Ex-alumni - XXXV cycle

Supervisor(s): Prof. Francesca Greselin , University of Milano-Bicocca; Prof. Ricardas Zitikis , University of Western Ontario, CA

Milano PhD Workshop 2024

The ECOSTATDATA PhD students are happy to announce the second edition of the Milano PhD Workshop , that will be held at the premises of the University of Milano-Bicocca, September 23-27, 2024.

The event is jointly organized with the PhD students in economics of the major universities in the Milanese area.

The program of the event is under construction and will be available shortly.

For details you can contact the local organizers:

PhD students' seminar series 2023-2024

We are very happy to announce this new initiative: the  ECOSTATDATA PhD Seminar Series!

This initiative aims to create a friendly environment where all PhD students at DEMS have the opportunity to present their own research or research proposal to obtain constructive feedback from peers and senior researchers.

Regular reminders before each presentation will be sent, and we really hope you will join this initiative. Your presence and support will be key to make this a success!

The Organizers 

@Angelica Bertucci  

@Ludovica De Carolis  

@Matteo Ferraro  

@Gregorio Ghetti  

@Lorena Popescu  

March 28, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker: Andrea Sorrentino

April 18, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Francesco Ferlaino

Field: Macroeconomics

May 09, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Luca Danese

Field: Bayesian Nonparametrics

May 16, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Angelica Bertucci

May 23, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Matteo Ferraro

May 30, 2024 Aula Seminari (U7 - 2104) 12:00

Speaker:  Lucia Tommasiello

June 6, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Mattia Longhi

June 13, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Claudia Sartirana

June 20, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker 1:  Ludovica De Carolis

Speaker 2:  Jiefeng Bi

Field: Bayesian Statistics

Past events (selected) 2018-2024

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca, joint with the Italian Society of Econometrics (SIdE), the Free University of Bolzano, the Fondazione Eni Enrico Mattei (FEEM), the International Association of Applied Econometrics (IAAE) and the Rimini Center for Economic Analysis (RCEA), have organized the 4th Italian Workshop on Econometrics and Empirical Economics (IWEEE 2024) - Climate and Energy Econometrics , at the Free University of Bolzano, during the period January 25-26, 2024. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Center for European Studies (CefES) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Bayesian Structural VAR, held by Prof. Fabio Canova , BI Norwegian Business School, during the period November 9-14, 2023. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by  Prof. Botond Szabo , Bocconi University, during the period October 5-27, 2021.

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Omiros Papaspiliopoulos , Bocconi University , during the period October 5-27, 2021. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca and the Fondazione Eni Enrico Mattei (FEEM), Milano, have organized the summer school on Frontiers of Energy Econometrics , at the Como Lake School of Advanced Studies, during the period September 13-17, 2021. Detailed information on the programme and the application procedure can be found on the summer school website:  https://toee.lakecomoschool.org/

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Rajen Shah , University of Cambridge, during the period October 5-30, 2020. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here.

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized and hosted the course Statistical Learning and Big Data, held by Prof.  Sharon Rosset , Tel Aviv University, during the period October 7-18 2019. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the 1 st  CefES International Conference on European Studies, to be held at the University of Milano-Bicocca, Building U6, on June 10th-11 th  2019. Details on this event can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the International Conference on Econometric Models of Climate Change, held at the University of Milano-Bicocca on August 29th-30 th  2019. Details on this event can be found  here .

Within the Seminar Series DEMS-ECOSTAT,  Prof. Peter M Robinson  (LSE),  has presented the paper titled “Long-range dependent curve time series” (joint with Degui Li and Han Lin Shang). Prof. Robinson is one of the most famous econometricians worldwide and has been in the editorial boards of the most influential journals in econometrics and statistics, from Econometrica to the Journal of Econometrics, from the Journal of the American Statistical Association to the Annals of Statistics. Peter Robinson’s presentation is available  here , while his paper is available  here . This  event  has been held on February 14th 2019, 12.00am, at the Aula del Consiglio, U7, fourth floor, Piazza dell’Ateneo Nuovo 1, 20126 - Milano.

Within the celebrative events of the Twentieth Anniversary of the University of Milano-Bicocca, the Department of Economics, Management and Statistics, in collaboration with the School for Graduate Studies, has organized the International Conference on  The Mathematics of Subjective Probability .  This event was held on September  3rd-5th  2018, at Room U4/2, Piazza della Scienza 1, 20126 - Milano.

Within the celebrative events of its Twentieth Anniversary, the University of Milano-Bicocca, in collaboration with its School for Graduate Studies, has organized the  Lectio Magistralis of Prof. Robert Engle  (NYU University), winner of the 2003 Nobel Memorial Prize in Economic Sciences, on “A Financial Approach to Environmental Risk”. This event was held on June 22nd 2018, 10.00am, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

The Center for European Studies (CefES-DEMS-UNIMIB), the PhD program in Economics and Statistics (ECOSTAT-UNIMIB), and the  Department of Economics, Management and Statistics  (DEMS-UNIMIB) have organized the one-day international conference on  Economic and Financial Implications of Climatic Change .  Two plenary sessions  on the economic and financial implications of climatic change have been organized on June 22 nd  2018, following Prof. Robert Engle’s talk, from 11.30am to 4.45pm, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

XXXVII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rg) offered in academic year 2022-23 (xxxvii cycle – ii year) for the curriculum in economics (eco):.

I term (October 2022 – December 2022)

-  Social Network Theory  (Instructor: Prof. F. Panebianco, Catholic University of Milano)

-  Applications of Game Theory  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Empirical Banking  (Instructor: Prof. Elena Beccalli, Catholic University of Milano)

-  Advanced Asset Pricing and Portfolio Management  (Instructor: Prof. A. Tarelli, Catholic University of Milano)

-  Empirical Corporate Finance  (Instructor: Prof. E. Croci, Catholic University of Milano)

-  Programming in Python  (Instructor: Prof. L. Viarengo, Catholic University of Milano)

II term (January 2023 – April 2023)

-  Spatial Models  (Instructor: Prof. S. Colombo, Catholic University of Milano)

-  Financial Frictions  (Instructor: Prof. D. Delli Gatti, Catholic University of Milano)

-  The Microeconomics of International Trade  (Instructor: Prof. V. Gattai, University of Milano-Bicocca)

-  Innovation and Industrial Evolution  (Instructor: Prof. C. Garavaglia, University of Milano-Bicocca)

-  Structural VAR Models  (Instructors: Proff. V. Colombo, G. Rivolta, Catholic University of Milano)

-  Applied Health Economics and Policy  (Instructors: Proff. G. Turati, E. Cottini, L. Salmasi, Catholic University of Milano)

Note:  the RG for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano (CUM). CUM is in charge of the timetable of each RG, whose updated version can be found  here . 

The following extra-RG are offered by ECOSTATDATA in the II term:

-  Expected Utility and Decision Theory  (Instructor: Prof. G. Cassese, University of Milano-Bicocca)

-  Estimated DSGE Models  (Instructor: Prof. Alice Albonico, University of Milano-Bicocca)

-  Authority and Delegation  (Instructor: Prof. Irene Valsecchi, University of Milano-Bicocca)

Note:  the timetable of the extra-RG is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum in Statistics (STAT):

I term (October 2022 – December 2022)

-  The Dependent Dirichlet Process and Related Models  (Instructors: Proff. F. Camerlenghi, B. Nipoti, University of Milano-Bicocca)

-  Some Issues in Statistical Modelling  (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

-  Empirical Bayes in Bayesian Inference  (instructor: Prof. S. Rizzelli, Catholic University of Milano)

-  Automated Machine Learning & Neural Architectural Search  (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

-  Deep Learning  (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Note:  the timetable of the RG for the curriculum STAT is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

II term (January 2023 – April 2023)

-  Databases for Structured and Unstructured Data – SQL  (POSTPONED) (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

-  Human-centered AI  (Instructor: Prof. F.M. Zanzotto, University of Roma-Tor Vergata)

Note:  the timetable of the RG for the curriculum BIDAB is available  here . 

XXXVIII cycle - Teaching activities - Year I (terms I - II - III - IV) - courses

The I term teaching activities start on 24 October 2022 and end on 23 December 2022. The I term exam session starts on 9 January 2023 and ends on 13 January 2023.

Note:  the timetable of the I term courses is available  here

The courses/modules offered during the I term for the curriculum Economics (ECO) are:

-  Computational Statistics I  (Instructor: Prof. G. Bertarelli, University of Pisa)

-  Mathematics – Linear algebra  (Instructor: Prof. N. Pecora, Catholic University of Milano)

-  Mathematics I  (Instructor: Prof. D. Visetti, University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli, University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo, Catholic University of Milano)

The courses/modules offered during term I for the curriculum Statistics (STAT) are:

-  Mathematical Analysis  (Instructors: Prof. C. Zanco, University of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Prof. L. Mascotto, University of Milano-Bicocca) 

The courses/modules offered during term I for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Programming in Python  (Instructor: Prof. M. Cesarini, University of Milano-Bicocca)

-  Architecture for Big Data Processing  (Instructor: Prof. V. Moscato, University of Napoli)

-  Architecture for Big Data Processing Lab  (Instructor: Prof. G. Sperlì, University of Napoli)

The II term teaching activities start on 16 January 2023 and end on 5 April 2023. The II term exam session starts on 17 April 2023 and ends on 21 April 2023. 

The courses/modules offered during the II term for the curriculum Economics (ECO) are:

-  Econometrics I  (Instructor: Prof. M. Manera, University of Milano-Bicocca)

-  Econometrics I – Tutorials  (Instructor: Dr. C. Cattaneo, European Institute on Economics and the Environment)

-  Econometrics II  (Instructor: Prof. M.L. Mancusi, Catholic University of Milano)

-  Econometrics II – Tutorials  (Instructor: Dr. E. Villar, Catholic University of Milano)

-  Econometrics III  (Instructor: Prof. A. Ugolini, University of Milano-Bicocca)

-  Econometrics III - Tutorials  (Instructor: Dr. D. Valenti, Fondazione Eni Enrico Mattei)

-  Microeconomics I  (Instructor: Prof. M. Mantovani, University of Milano-Bicocca)

-  Microeconomics I – Tutorials  (Instructor: Dr. F. Campo, University of Milano-Bicocca)

-  Microeconomics II  (Instructtor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics II – Tutorials  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics III  (Instructor: Prof. L. Colombo, Catholic University of Milano)

-  Microeconomics III – Tutorials  (Instructor: Dr. D. Bosco, University of Milano-Bicocca)

-  Microeconomics IV  (Instructor: Prof. P. Bertoletti, University of Milano-Bicocca)

-  Microeconomics IV – Tutorials  (Instructor: Dr. G. Crea, University of Pavia)

Note:  the timetable of the II term courses for the curriculum ECO is available  here .

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

-  Probability I & II  (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

-  Stochastic Processes  (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

-  R for Data Science  (Instructor: Prof. A. Gilardi, University of Milano-Bicocca)

-  Statistical Inference I  (Instructor: Prof. A. Caponera, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum STAT is available  here .

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BIDAB) are:

-  Probability  (Instructor: Prof. A. Di Brisco, University of Piemonte Orientale)

-  Statistical Inference I  (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum BIDAB is available  here .

The III term teaching activities start on 26 April 2023 and end on 7 July 2023. The III term exam session starts on 17 July 2023 and ends on 21 July 2023. 

The courses/modules offered during the III term for the curriculum Economics (ECO) are:

- Macroeconomics I (Instructor: Prof. G. Femminis, Catholic University of Milano)

- Macroeconomics II (Instructor: Prof. A. Albonico, University of Milano-Bicocca)

- Macroeconomics III (Instructor: Prof. R. Masolo, Catholic University of Milano)

- Macroeconomics IV (Instructor: Dr. B. Barbaro, University of Milano-Bicocca)

-  Computational Statistics II  (Instructor: Prof. A. Pini, Catholic University of Milano)

-  Research Methods  (Instructors: Prof. T. Colussi, Catholic University of Milano; Prof. K. Aktas, University of Milano-Bicocca)

- Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori, Catholic University of Milano)

- Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz, Catholic University of Milano)

- Finance III – Banking (Instructors: Proff. M. Migliavacca, F. Pampurini, Catholic University of Milano)

Note:  the timetable of the III term courses for the curriculum ECO is available here .

The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

-  Statistical Inference II  (Instructor: Prof. A. Solari, University of Milano-Bicocca)

- Bayesian Statistics (Instructors: Prof. R. Argiento, University of Bergamo; Proff. B. Nipoti, T. Rigon, University of Milano-Bicocca)

- Data Management (CANCELLED)

Note:  the timetable of the III term courses for the curriculum STAT is available here .

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Technology and Innovation Management (Instructors: Proff. S. Torrisi, L. D'Agostino, F. Di Pietro, M. Guerzoni, University of Milano-Bicocca)

- Machine Learning (Instructor: Prof. L. Malandri, University of Milano-Bicocca)

- Natural Language Understanding (CANCELLED)

-  Social Media Analytics  (Instructor: Prof. R. Boselli, University of Milano-Bicocca)

Note:  the timetable of the III term courses for the curriculum BIDAB is available here .

The IV term teaching activities start on 4 September 2023 and end on 20 October 2023. The IV term exam session starts on 23 October 2023 and ends on 27 October 2023. 

Note:  the timetable of the IV term courses is under construction and is currently shared with all the ECOSTATDATA students, who can monitor online any updates/modifications.

The courses/modules offered during the IV term for the curriculum Statistics (STAT) are:

- Statistical Learning (POSTPONED)

- Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano)

- Statistical Modelling II (Instructor: Prof. F. Greselin, University of Milano-Bicocca)

- Statistical Modelling III (Instructor: Dr. S. Verzillo, European Commission - Joint Research Center)

- Statistical Modelling IV (Instructors: Prof. F. Pennoni, University of Milano-Bicocca; Prof. F. Bartolucci, University of Perugia)

The courses/modules offered during the IV term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Statistical Inference II (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

- Explainable AI for Business Value (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

- Deep Learning and Computer Vision for Business (Instructor: Prof. E. Frontoni, Polytechnic University of Marche, TBC)

XXXVIII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rgs) offered in academic year 2023-24 (xxxviii cycle – ii year) for the curriculum economics (eco):.

I term (October 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the RGs for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano. Detailed information on each RG and its timetable can be found  here . 

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Statistics (STAT):

I term (November 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum STAT is shared online (via Google Calendar) with students officially enrolled in the PhD program. 

- RG Approximate Bayesian Computational Methods (Instructor: Dr. A. Fasano , Catholic University of Milano)

- RG Automated Machine Learning & Neural Architectural Search (Instructor: Prof. A. Candelieri , University of Milano-Bicocca)

- RG Spatio-temporal Data (Instructors: Prof. R. Borgoni  and Dr. P. Maranzano , University of Milano-Bicocca)

- RG Some Issues on Statistical Modelling (Instructor: Prof. R. Borgoni , University of Milano-Bicocca)

- RG Deep Learning (Instructor: Prof. M. Borrotti , University of Milano-Bicocca)

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

I term (November 2023 - December 2023) and   II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum BIDAB is shared online (via Google Calendar) with students officially enrolled in the PhD program.

- RG Natural Language Processing (Instructor: Dr. A. Seveso , University of Milano-Bicocca)

- RG Generative AI (Instructor: Dr. Navid Nobani , University of Milano-Bicocca)

XXXIX cycle - Teaching activities - Year I (terms I - II - III) - courses

The I term teaching activities start on 23 October 2023 and end on 22 December 2023. The I term exam session starts on 8 January 2024 and ends on 12 January 2024.

Note:  the timetable of the I term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

-  Mathematics – Linear algebra  (Instructor: Dr. N. Pecora , Catholic University of Milano)

-  Mathematics I  (Instructor: Dr. D. Visetti , University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli , University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo , Catholic University of Milano)

- Microeconomics I (Instructor: Prof. M. Mantovani , University of Milano-Bicocca)

-  Mathematical Analysis I-II-III  (Instructors: Dr. J. Somaglia , Polytechnic of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Dr. L. Mascotto , University of Milano-Bicocca) 

-  Programming in Python  (Instructor: Dr. M. Cesarini , University of Milano-Bicocca)

-  Architecture for Big Data Processing & Lab  (Instructors: Prof. V. Moscato and Dr. G. Sperlì , University of Napoli)

The II term teaching activities start on 15 January 2024 and end on 27 March 2024. The II term exam session starts on 8 April 2024 and ends on 12 April 2024.

Note:  the timetable of the II term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics II (Instructor: Prof. M. Gilli , University of Milano-Bicocca)

- Microeconomics III (Instructors: Prof. L. Colombo and Dr. M. Magnani , Catholic University of Milano)

- Microeconomics IV (Instructors: Prof. P. Bertoletti , University of Milano-Bicocca, and Dr. G. Crea , University of Pavia)

- Econometrics I (Instructors: Prof. M. Manera , University of Milano-Bicocca, and Dr. C. Cattaneo , European Institute on Economics and the Environment)

- Econometrics II (Instructors: Dr. A. Ugolini , University of Milano-Bicocca, and Dr. D. Valenti , Polytechnic of Milano)

- Econometrics III (Instructors: Prof. M.L. Mancusi and Dr. E. Villar , Catholic University of Milano)

- Computational Statistics I (Instructor: Dr. G. Bertarelli , University of Venezia)

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

- Probability I-II (Instructor: Prof. F. Camerlenghi , University of Milano-Bicocca)

- Stochastic Processes (Instructor: Dr. B. Buonaguidi , Catholic University of Milano)

- Statistical Inference I (Instructor: Dr. A. Caponera , Luiss Guido Carli University) 

- R for Data Science (Instructor: Dr. A. Gilardi , Polytechnic of Milano)

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BiDAB) are:

- Probability (Instructor: Prof. A. Di Brisco , University of Piemonte Orientale)

-  Statistical Inference I (Instructor: Dr. R. Ascari , University of Milano-Bicocca)

The III term teaching activities start on 15 April 2024 and end on 5 July 2024. The III term exam session starts on 15 July 2024 and ends on 19 July 2024.

- Computational Statistics II (Instructor: Prof. A. Pini , Catholic University of Milano)

- Macroeconomics I  (Instructor: Prof. G. Femminis , Catholic University of Milano)

- Macroeconomics II  (Instructors: Prof. A. Albonico , University of Milano-Bicocca)

- Macroeconomics III (Instructors: Dr. B. Barbaro , University of Milano-Bicocca and Prometeia)

- Macroeconomics IV (Instructors: Dr. R. Masolo , Catholic University of Milano)

- Research Methods (Instructors: Dr. S. Ghisolfi and Prof. T. Colussi , Catholic University of Milano)

The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

- Statistical Inference II (Instructor: Prof. A. Solari , University of Venezia)

- Principles of Bayesian Statistics (Instructor: Prof. B. Nipoti , University of Milano-Bicocca)

- Bayesian Computations (Instructor: Dr. T. Rigon , University of Milano-Bicocca) 

- Bayesian Modelling (Instructor: Prof. R. Argiento , University of Bergamo)

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Statistical Inference II (Instructor: Dr. R. Ascari , University of Milano-Bicocca)

- Social Media Analytics (Instructor: Dr. R. Boselli , University of Milano-Bicocca)

- Machine Learning (Instructor: Dr. L. Malandri , University of Milano-Bicocca)

- Technology and Innovation Management I (Instructor: Prof. S. Torrisi , University of Milano-Bicocca)

- Technology and Innovation Management II (Instructor: Prof. M. Corsino , University of Milano-Bicocca)

- Technology and Innovation Management III (Instructor: Dr. F. Di Pietro , University of Milano-Bicocca)

- Technology and Innovation Management IV (Instructor: Prof. M. Guerzoni , University of Milano-Bicocca)

XXXIX cycle - Teaching activities - Year II (terms I-II) - reading groups

The I term reading groups start on October 14, 2024 and end on December 20, 2024. 

Note:  The list of I term reading groups will be published soon and the timetable will be shared online (via Google Calendar) with all students officially enrolled in the PhD program.

Calendar 2024-2025

The I term teaching activities start on October 23, 2024 and end on December 20, 2024. The I term exam session starts on January 7, 2025 and ends on January 14, 2025.

The II term teaching activities start on January 20, 2025 and end on April 4, 2025. The II term exam session starts on April 11, 2025 and ends on April 16, 2025.

The III term teaching activities start on April 22, 2025 and end on July 4, 2025. The III term exam session starts on July 14, 2025 and ends on July 18, 2025.

The IV term teaching activities (curriculum STAT only) start on September 8, 2025 and end on October 26, 2025. The IV term exams are scheduled at the end of each module.

The resit exams (all curricula) are typically scheduled in the period September-October 2025. 

XL cycle - Teaching activities - Year I (term I) - courses

Note:  The list of I term courses will be published soon and the timetable will be shared online (via Google Calendar) with all students officially enrolled in the PhD program.

-  Mathematics – Linear algebra  (Instructor:  A. Mainini , Catholic University of Milano)

-  Mathematics I  (Instructor:  D. Visetti , University of Milano-Bicocca);

-  Mathematics II  (Instructor:   F. Cavalli , University of Milano-Bicocca);

-  Mathematics III  (Instructor:  M. Longo , Catholic University of Milano)

- Microeconomics I (Instructor:  M. Mantovani , University of Milano-Bicocca)

-  Mathematical Analysis I  (Instructor: E. Miglierina , Catholic University of Milano)

-  Mathematical Analysis II  (Instructor: C.A. De Bernardi , Catholic University of Milano)

-  Mathematical Analysis III  (Instructor: F. Battistoni , Catholic University of Milano)

-  Programming in Python  (Instructor: M. Cesarini , University of Milano-Bicocca)

-  Architecture for Big Data Processing  (Instructor: V. Moscato , University of Napoli)

-  Architecture for Big Data Processing Lab  (Instructor: G. Sperlì , University of Napoli)

ECTSTATP01 - Econometrics and business statistics PhD program

People Search

Phd program economics and statistics.

Would you like to work intensively on economic and socio-political questions and develop new research methods?

Apply online

The PhD Program in Economics and Statistics is offered jointly by the University of Innsbruck and the University of Linz, leading European institutions for empirical economics and data science as well as behavioral and experimental economics. The programme is open to students from around the world, provided they hold a Master’s degree or equivalent. Students become competent researchers within the research community investigating economic and socio-political problems in depth and developing new research methods. The topic of a dissertation should be aligned with the research interests of potential supervisors. Please check the faculty pages to learn about the research interests of potential supervisors. It is highly recommended to contact potential supervisors before finally applying to the programme.

Study Code UC 794 355 xxx

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Graduates acquire the ability

• to write scientific papers that meet the international standards of peer-reviewed publications;
• to contribute to the international research network; to develop independent research programmes with scientific integrity and to successfully introduce and implement these at national and international research funding agencies;
• communicate with their professional environment, the scientific community and society in general about their specialist field.

The work of the graduates of the PhD Program in Economics and Statistics mainly consist of:

• to conduct theoretical and empirical research in economics or statistics and to publish internationally in economics or statistics,
• to apply theoretical and empirical research methods to solve (economic-political) problems and to develop them further for new problems.

Graduates will find their professional field of activity among other things:

• at universities,
• in research institutions and other post-secondary educational institutions,
• in research departments of the European Central Bank, the Oesterreichische Nationalbank, of other banks, rating agencies, regional development companies, market research institutions as well as in other companies and commercial organisations,
• in research departments of international organizations such as the OECD (Organization for Economic Development and Cooperation), EU (European Union), IMF (International Monetary Fund) or the World Bank,
• in research departments of public institutions, interest groups and NGOs (non-governmental organisations),
• in politics and the media,
• in the national statistical institutes, in particular Statistics Austria and the regional statistical offices.

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Fur­ther edu­ca­tion

Career Ser­vice

The next online session takes place on December 11th, 2023 at 3pm. You can attend the session via the follwing link:  https://webconference.uibk.ac.at/b/jud-egz-szc-fcy

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• Accounting & Management
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Students in our PhD programs are encouraged from day one to think of this experience as their first job in business academia—a training ground for a challenging and rewarding career generating rigorous, relevant research that influences practice.

Our doctoral students work with faculty and access resources throughout HBS and Harvard University. The PhD program curriculum requires coursework at HBS and other Harvard discipline departments, and with HBS and Harvard faculty on advisory committees. Faculty throughout Harvard guide the programs through their participation on advisory committees.

How do I know which program is right for me?

There are many paths, but we are one HBS. Our PhD students draw on diverse personal and professional backgrounds to pursue an ever-expanding range of research topics. Explore more here about each program’s requirements & curriculum, read student profiles for each discipline as well as student research , and placement information.

The PhD in Business Administration grounds students in the disciplinary theories and research methods that form the foundation of an academic career. Jointly administered by HBS and GSAS, the program has four areas of study: Accounting and Management , Marketing , Strategy , and Technology and Operations Management . All areas of study involve roughly two years of coursework culminating in a field exam. The remaining years of the program are spent conducting independent research, working on co-authored publications, and writing the dissertation. Students join these programs from a wide range of backgrounds, from consulting to engineering. Many applicants possess liberal arts degrees, as there is not a requirement to possess a business degree before joining the program

The PhD in Business Economics provides students the opportunity to study in both Harvard’s world-class Economics Department and Harvard Business School. Throughout the program, coursework includes exploration of microeconomic theory, macroeconomic theory, probability and statistics, and econometrics. While some students join the Business Economics program directly from undergraduate or masters programs, others have worked in economic consulting firms or as research assistants at universities or intergovernmental organizations.

The PhD program in Health Policy (Management) is rooted in data-driven research on the managerial, operational, and strategic issues facing a wide range of organizations. Coursework includes the study of microeconomic theory, management, research methods, and statistics. The backgrounds of students in this program are quite varied, with some coming from public health or the healthcare industry, while others arrive at the program with a background in disciplinary research

The PhD program in Organizational Behavior offers two tracks: either a micro or macro approach. In the micro track, students focus on the study of interpersonal relationships within organizations and the effects that groups have on individuals. Students in the macro track use sociological methods to examine organizations, groups, and markets as a whole, including topics such as the influence of individuals on organizational change, or the relationship between social missions and financial objectives. Jointly administered by HBS and GSAS, the program includes core disciplinary training in sociology or psychology, as well as additional coursework in organizational behavior.

Accounting & Management  

Business economics  , health policy (management)  , marketing  , organizational behavior  , strategy  , technology & operations management  .