phd economics and statistics

Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

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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The doctoral program in Economics at Harvard University is one of the leading programs in the world. Supported by a diverse group of faculty who are top researchers in their fields and fueled by a vast array of resources, the PhD program is structured to train and nurture students to become leading economists in academia, government agencies, the technology industry, finance and banking, and global policy organizations.

Harvard University and the Department of Economics are regularly ranked amongst the top programs in the world, and the consistency of success among our graduates is inspiring. We have educated several foreign heads of state, Nobel Prize Winners, Clark Medal Winners, MacArthur Fellowship Recipients - many of whom have returned to Harvard to offer their expertise and brilliance in shaping and nurturing our students.  Learn more about where we place our  graduates  and explore our  Program  to find out if a PhD in Economics is a good fit for you. 

Program Requirements

As a PhD student in the Economics program, students will spend the first two years in the program engaged in rigorous coursework designed to develop a foundational understanding of economics. In the following years, students transition to research under the guidance of strong faculty mentorship and participate in field workshops. In the final year, students conduct independent research and complete a dissertation.

The department of Economics at Harvard University is committed to seeking out and mentoring scholars who wish to pursue a rigorous and rewarding career in economic research. Our graduates are trailblazers in their fields and contribute to a diverse alumni community in both the academic and non-academic sectors. We invite you to learn more and apply to the PhD program in Economics. 

Financial Support

Students have access to a variety of funding and financial support opportunities.

• Research Funding
• Teaching Fellowships and assistants
• Additional external and internal resources

Learn more about financial support

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The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

PhD Program Requirements

PhD Program Admissions Process

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• AM in Statistics
• Stat 300: Research in Statistics
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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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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 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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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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• Dissertation Areas and Joint PhD Programs
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PhD in Econometrics and Statistics

• PhD in Microeconomics
• PhD in Finance
• PhD in Management Science and Operations Management
• PhD in Marketing
• Joint Program in Financial Economics
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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

Assistant 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

Mladen Kolar

Associate Professor of Econometrics and Statistics

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 Foundation 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

A Network of Support

Booth’s Econometrics and Statistics group has been partnering with several (data science and interdisciplinary) research centers and institutes that facilitate the translation of research into practice. Through these venues, our PhD students can foster a strong research community and find additional resources including elective courses, funding for collaborative student work, and seminars with world-renowned scholars.

Data Science Institute at the University of Chicago The Data Science Institute executes the University of Chicago’s bold, innovative vision of Data Science as a new discipline by advancing interdisciplinary research, partnerships with industry, government, and social impact organizations. Center for Applied Artificial Intelligence The Center for Applied AI incubates transformative projects that fundamentally shape how humans use AI to interact with each other and the world. The Center’s innovative research uses machine learning and behavioral science to investigate how AI can best be used to support human decision-making and improve society. Toyota Technological Institute at Chicago Toyota Technological Institute at Chicago (TTIC) is a philanthropically endowed academic computer science institute, dedicated to basic research and graduate education in computer science. Its mission is to achieve international impact through world-class research and education in fundamental computer science and information technology. The Institute is distinctive to the American educational scene in its unique combination of graduate education and endowed research.

The Becker Friedman Institute for Economics With a mission of turning evidence-based research into real-world impact, the Becker Friedman Institute brings together the University of Chicago’s economic community. Ideas are translated into accessible formats and shared with world leaders tasked with solving pressing economic problems. Committee on Quantitative Methods in Social, Behavioral and Health Sciences This is an interdisciplinary community of faculty and students interested in methodological research in relation to applications in social, behavioral, and health sciences. The goals are to create an intellectual niche, exchange research ideas, facilitate research collaborations, share teaching resources, enhance the training of students, and generate a collective impact on the University of Chicago campus and beyond. The Institute for Data, Econometrics, Algorithms, and Learning The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments.

The Fama-Miller Center for Research in Finance Tasked with pushing the boundaries of research in finance, the Fama-Miller Center provides institutional structure and support for researchers in the field. James M. Kilts Center for Marketing The Kilts Center facilitates faculty and student research, supports innovations in the marketing curriculum, funds scholarships for MBA and PhD students, and creates engaging programs aimed at enhancing the careers of students and alumni.

Scholarly Publications

Our PhD students' research has been published in top journals including Econometrica, Journal of Royal Statistical Society, Journal of Econometrics, Neural Information Processing Systems and Journal of Machine Learning Research. Below is a recent list of publications and working papers authored by our PhD students. Modeling Tail Index with Autoregressive Conditional Pareto Model Zhouyu Shen, Yu Chen and Ruxin Shi, Journal of Business and Economic Statistics, (40) 2022 Online Learning to Transport via the Minimal Selection Principle Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan, Proceedings of 35th Conference on Learning Theory (COLT), (178) 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting Boxin Zhao, Samuel Wang and Mladen Kolar, Journal of Machine Learning Research, (23) 2022 Approximate Bayesian Computation via Classification Yuexi Wang, Tetsuya Kaji and Veronika Rockova, Journal of Machine Learning Research (In press), 2022 Reversible Gromov-Monge Sampler for Simulation-Based Inference YoonHaeng Hur, Wenxuan Guo and Tengyuan Liang, Journal of the American Statistical Association (R&R). 2021. Data Augmentation for Bayesian Deep Learning Yuexi Wang, Nicholas Polson and Vadim Sokolov, Bayesian Analysis (In press), 2022 Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Boxiang Lyu, Zhaoran Wang, Mladen Kolar and Zhuoran Yang, In Proceedings of the 39th International Conference on Machine Learning (ICML), (162) 2022 Optimal Estimation of Gaussian DAG Models Ming Gao, Wai Ming Tai and Bryon Aragam, International Conference on Artificial Intelligence and Statistics (AISTATS), (151) 2022 Multivariate Change Point Detection for Heterogeneous Series Yuxuan Guo, Ming Gao, and Xiaoling Lu, Neurocomputing, (510) 2022 Disentangling Autocorrelated Intraday Returns Rui Da and Dacheng Xiu, Journal of Econometrics (R&R), 2021 When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility Rui Da and Dacheng Xiu, Econometrica, (89) 2021 Efficient Bayesian Network Structure Learning via Local Markov Boundary Search Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Structure Learning in Polynomial Time: Greedy Algorithms, Bregman Information, and Exponential Families Goutham Rajendran, Bohdan Kivva, Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Variable Selection with ABC Bayesian Forests Yi Liu, Yuexi Wang and Veronika Rockova, Journal of the Royal Statistical Association: Series B, (83) 2021  A Polynomial-time Algorithm for Learning Non-parametric Causal Graphs Ming Gao, Yi Ding, and Bryon Aragam, Advances in Neural Information Processing System (NeurIPS), (33) 2020 Uncertainty Quantification for Sparse Deep Learning Yuexi Wang and Veronika Rockova, International Conference on Artificial Intelligence and Statistics (AISTATS), (2018) 2020 Direct Estimation of Differential Functional Graphical Models Boxin Zhao, Samuel Wang and Mladen Kolar, Advances in neural information processing systems (NeurIPS), (32) 2019

The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation Chen Tong, Zhuo Huang, Tianyi Wang, and Cong Zhang, Journal of Empirical Finance (R&R), 2022

Spotlight on Research

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

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. "

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 Rui Da

Chaoxing Dai

Wenxuan Guo

Shuo-Chieh Huang

Shunzhuang Huang So Won (Sowon) Jeong

Boxiang (Shawn) Lyu 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!

College of Arts & Sciences

Ph.D. Program

Welcome to the phd program.

The Georgetown University Economics Ph.D. program is a full-time program designed for students who seek research careers at the forefront of economic science. The Ph.D. program provides an excellent training environment for future scholars. Located in the nation’s capital, it is ideally situated for both students specializing in pure science and those who wish to study policy informed by science.

Main Contacts

• Julius Shapiro serves as the PhD Program Coordinator. Please contact him first with any questions related to the PhD Program.
• Professor  Toshihiko Mukoyama  serves as Director of Graduate Studies and is responsible for the PhD Program.

Information for Applying to the PhD Program

• Frequently Asked Questions
• Supplementary Form

Biennial Conference

Recent Placements

Information for Current Students

Current PhDs Students

Razin Lecture and Prize

General Information

• Directions to the Department
• Housing Information
• Current Job Market Candidates
• Previous Job Market Candidates

Our PhD Students

• Current PhD Student Profiles

Program Requirements

• Course of Study
• Core Courses
• Field Courses
• Policies and Procedures

Curriculum and Thesis

In their first and second years, PhD students are required to complete a series of core classes, coursework in their major and minor fields of study, and an advanced research methods course before proceeding to the thesis-writing stage.

Core courses

Students must satisfy the requirements in at least 10 of 12 half-semester first-year core courses (14.384 and 14.385 are considered second-year courses). The requirements can be met by earning a grade of B or better in the class or by passing a waiver exam.

Waiver exams are offered at the start of the semester in which the course is offered and graded on a pass-fail basis. Students who receive a grade of B- or below in a class can consult the course faculty to determine whether to take the waiver exam or re-take the course the following year. These requirements must all be satisfied before the end of the second year.

Course list

• 14.121: Microeconomic Theory I
• 14.122: Microeconomic Theory II
• 14.123: Microeconomic Theory III
• 14.124: Microeconomic Theory IV
• 14.380: Statistical Methods in Economics
• 14.381: Estimation and Inference for Linear Causal and Structural Models
• 14.382*: Econometrics
• 14.384*: Time Series Analysis (2nd year course)
• 14.385*: Nonlinear Econometric Analysis (2nd year course)
• 14.451: Dynamic Optimization Methods with Applications
• 14.452: Economic Growth
• 14.453: Economic Fluctuations
• 14.454: Economic Crises

*Courses 14.382, 14.384, and 14.385 are each counted as two half-semester courses.

Most students will also take one or more field courses (depending on whether they are waiving core courses) during their first year. Feel free to ask your graduate research officer, field faculty, and advanced students for advice on how you structure your first-year coursework.

Second year students must also successfully complete the two-semester course 14.192: Advanced Research Methods and Communication. The course, which is graded on a pass-fail basis, guides students through the process of writing and presenting the required second-year research paper.

Major field requirement

By the end of year two, PhD students must complete the requirements for two major fields in economics. This entails earning a B or better in two designated courses for each field. Some fields recommend additional coursework or papers for students intending to pursue research in the field.

Major fields must be declared by the Monday following the spring break of your second year. Your graduate registration officer must approve your field selections.

Minor field requirement

PhD students are also required to complete two minor fields, taking two courses in each field and earning a grade of B or better. Your graduate registration officer must approve your field selections.

Minor coursework is normally completed by the end of year two, but in some cases students can defer the completion of one field until after general exams. Students must consult with their graduate registration officer before making a deferment.

Options for minor fields include the eleven economics major fields, plus computation and statistics (from the interdisciplinary PhD in Economics and Statistics).

Students who wish to satisfy one of the minor field requirements by combining two courses from different fields–for example, environmental economics and industrial organization II–can petition the second-year graduate registration officer for permission.

At least one minor field should be from the department’s standard field list.

The fields in which the Department offers specialization and the subjects that will satisfy their designation as a minor field are given in the chart below. Some fields overlap so substantially that both cannot be taken by a student. In any event, the same subject cannot be counted towards more than a single minor field. Students must receive the approval of their Graduate Registration Officer for their designated major and minor fields.

List of fields

• Development
• Econometrics
• Industrial organization
• International
• Macroeconomics
• Organizational
• Political economy
• Public finance
• Computation and statistics (minor only)

Subjects satisfying major and minor requirements

Advanced economic theory.

Minor: Any subset adding up to two full semesters from 14.125, 14.126, 14.127, 14.130, 14.137, 14.147, 14.160, 14.281 and Harvard Ec 2059. Major: At least two of 14.125, 14.126, 14.281, and Harvard Ec 2059. Recommended for major: 14.126, 14.281, and at least one of 14.125, 14.127, 14.130, 14.147, and Harvard Ec 2059.

Econometrics and Statistics

Minor: 14.382 in addition to one of 14.384 or 14.385. Major: Any one of 14.386, 14.387, 14.388 in addition to one of 14.384 or 14.385. Recommended for major: 14.384 and 14.385. *Dual PhD in Economics and Statistics has an additional requirement of 14.386.

Economic Development

Major and minor: 14.771 and 14.772 or 14.773

Minor: Any two of 14.416J, 14.440J, 14.441J, 14.442J, 14.448. Major: 14.416J and 14.441J

Industrial Organization

Minor: 14.271 and 14.272 or 14.273. Major: 14.271 and 14.272 or 14.273. Recommended for major: 14.271, 14.272, and 14.273.

International Economics

Major and minor: 14.581 and 14.582

Labor Economics

Major: 14.661 and 14.662A. Minor: Two subjects chosen from 14.193, 14.661, and 14.662

Monetary Economics

Major and minor: Two subjects chosen from 14.461, 14.462, and 14.463

Organizational Economics

Major and minor: 14.282 and one of 14.283-284, 14.441J, or an approved substitute

Political Economy

Major and minor: 14.770 and 14.773

Public Economics

Major and minor: 14.471 and 14.472

General exams

MIT requires doctoral candidates to complete an advanced course of study that includes general exams at its completion. Beginning in 2019-20, the Economics Department will operationalize this requirement to include successful completion of: the core and other required courses; course exams and other requirements of courses in each of a student’s two major and two minor fields; the written research paper and oral presentation components of 14.192. Students may present for the general exams while having one remaining minor field to complete. The faculty will review these components together with the candidate’s overall course record to determine whether students have passed the general exam requirement and can proceed to the thesis writing stage.

Typical course schedule

Math Camp begins on the second Monday in August.

Fall Semester

14.121/14.122 (Micro Theory I/II) 14.451/14.452 (Macro Theory I/II) 14.380/14.381 (Statistical Method in Economics & Applied Econometrics) Field Course (major or minor)

Spring Semester

14.123/14.124 (Micro Theory III/IV) 14.453/14.454 (Macro Theory III/IV) 14.382 (Econometrics) Field Course (major or minor)

2-3 Field Courses 14.192 (Advanced Research and Communication) 14.384  or  14.385 (Advanced Econometrics)

3 Field Courses 14.192 (Advanced Research and Communication)

Years 3 and up

Field workshop Field lunch Thesis writing

Upon satisfying the core and field requirements, PhD candidates embark on original research culminating in a completed dissertation. A PhD thesis normally consists of three research papers of publishable quality. The thesis must be approved by a student’s primary and secondary thesis advisors, and by an anonymous third reader. These three faculty members will be the candidate's thesis committee and are responsible for its acceptance. Collaborative work is acceptable and encouraged, but there must be at least one paper in the dissertation without a co-author who was a faculty member when the research started.

Criteria for satisfactory progress

Third-year students.

• Meet regularly with their advisor
• Participate consistently in their primary field advising lunch, their primary field workshop, and the third-year student research lunch
• Complete their third-year paper
• Participate in third-year meetings organized by the thesis graduate research officer

Students should present on their research in progress at least once in both the third-year student research lunches and their field advising lunch. Presentations provide opportunities for early and broad feedback on research ideas and the chance to develop oral presentation skills. Research ideas or early stage work in progress is encouraged and expected.

Fourth-year and later students

• Participate consistently in their primary field advising lunch and their primary field workshop
• Present at least once per year in their field advising lunch or field workshop. A presentation each semester in the field advising lunch is strongly recommended by most fields; consult your advisors for more information

Satisfactory progress toward a dissertation will be evaluated based on progress assessments by the student’s primary advisor, regular participation in the lunches and workshops, and field lunch or workshop presentations that show continued progress.

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• Prospective Students
• Current Students
• MSc Offer Holders

PhD in Statistics

Study with leading statisticians at a world-class university

  Applications for entry 2024/25 are open  

Funding deadlines: 15 January 2024 (Applications received by this date will be considered for available studentships; we may also be able to consider applications received by the end of March for funding, but this is not guaranteed.) Final application deadline: 23 May 2024  

How to Apply

A PhD offers the chance to undertake a substantial piece of supervised work that is worthy of publication and which makes an original contribution to knowledge in a particular field. Our PhD programme is designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of your topic of interest. 

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from our long history of active contributions to research and teaching in statistics. 

Our core research areas are:

• Data science
• Probability in finance and insurance
• Social statistics
• Time series and statistical learning

If you have any questions about our MPhil/PhD Statistics programme, please  email the Research Manager .  

Research environment

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. It has a rich research portfolio covering core areas of statistical inference and real applications, particularly in the economic, financial and actuarial, social and industrial arenas. The close collaboration with other LSE departments, our London location and strong international partnerships are reflected in the research life of the Department of Statistics through the members of staff, PhD students, postdoctoral research fellows and the thriving visitor and seminar programmes.

Research in the department is concentrated in the following areas and PhD proposals should normally be linked to one of these areas:

Data Science

Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.

Possible areas of research include:

• Bayesian inference and predictions
• Functional data analysis
• High-dimensional statistics
• Machine and statistical learning for relational data
• Network data models, inference and predictions
• Optimisation and machine learning
• Reinforcement learning
• Statistical learning methods in precision medicine
• Statistical models and inference for ranking data
• Stochastic models of epidemic processes
• Stochastic optimisation methods
• Stochastic processes in econometrics and finance

For more information about potential supervisors and their areas of interest, visit the Data Science research group .

Probability in Finance and Insurance

PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics.  Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc. 

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

Suggested research areas of PhD research projects include:

• Energy markets
• Excursions of Lévy processes and applications in finance and insurance
• Financial market with frictions
• Information asymmetry
• Interface between insurance and finance
• Lévy processes
• Optimal stopping
• Point processes in insurance and credit risk
• Quantile options and options based on occupation times
• Stochastic analysis and its applications in financial mathematics
• Stochastic control and analysis of partial differential equations in mathematical finance

This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the  Probability in Finance and Insurance research group . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal. 

Social Statistics

PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

• Analysis of complex survey data
• Disclosure risk assessment and statistical disclosure control
• Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
• Latent transition and latent class models for modelling diagnostic tests
• Latent variable models and structural equation models for categorical data
• Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
• Modelling response strategies and detection of outliers in educational and behavioural sciences
• Multilevel simultaneous equations modelling of correlated social processes

For more details about potential supervisors and their areas of interest, visit the  Social Statistics research group . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.

Time Series and Statistical Learning

PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.

Suggested PhD research areas include:

• Automating statistical advice
• Change detection for complex data
• Dimension reduction and factor modelling
• Estimation of stochastic volatility models
• Financial econometrics
• Functional data analysis including functional time series
• High-dimensional time series analysis
• High-dimensional variable selection
• Infectious disease modelling
• Inference for sequential data including change detection in multiple data streams
• Network time series analysis
• Nonparametric and semiparametric regression
• Non-stationary time series analysis
• Reinforcement learning for time-dependent data
• Robust statistical analysis for high-dimensional data
• Shape-constrained methods
• Spatial econometrics modelling
• Spatio-temporal modelling
• Statistical analysis of high-dimensional multi-type recurrent events

For more information, please see the  Time Series and Statistical Learning research group . 

PhD Programme Structure

PhD Your application

PhD Funding Opportunities

FAQs Frequently Asked Questions

PhD Handbook Our guide for current students

Past PhD students A listing of our PhD graduates

Visiting Research Students How to apply

PhD Academy A dedicated space for PhD students

Graduate Career Destinations What our PhD graduates have done next

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

All studies Student advisory service

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.

Graduates tracking : Shows which occupational fields students enter after graduation

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

Faculty of Economics and Statistics Examination Office Information for students with disabilities

From the field

Preis für beste Nachwuchsarbeit gewonnen

Myriam Zollner, Absolventin des Masterstudiums Nachhaltige Regional- und Destinationsentwicklung, hat im Juni bei der Jahrestagung 2023 des „Arbeitskreis Tourismusforschung in der Deutschen Gesellschaft für Geographie (DGfG) e.V.“ in Saarbrücken (D) den „Young Researcher Award“ für die beste Masterarbeit gewonnen.

Inten­­sität des Kon­­takts zu Geflüch­­teten beein­­flusst Wahl­­ergeb­­nis

2015 fand die Landtagswahl in Oberösterreich auf dem Höhepunkt der Flüchtlingskrise in Europa statt. Der Wirtschaftsforscher Andreas Steinmayr untersuchte in diesem Zusammenhang, wie sich die Präsenz von Geflüchteten auf die Stimmabgabe für rechtspopulistische Parteien auswirkt. Ergebnis: Wo Geflüchtete langfristig untergebracht waren, reduzierte sich der Stimmenanteil für die FPÖ.

Finanz­pro­dukte: Warum wir Risi­ko falsch be­wer­ten

In einem groß angelegten Experiment hat ein Forscherteam mit Beteiligung der Uni Innsbruck erhoben, wie Laien und Finanzexperten das Risiko von Finanzprodukten wahrnehmen. Ein Fazit: Die Forscher fordern mehr Information bei Finanzprodukten. Die Studie erschien kürzlich im Fachjournal „Management Science“.

Falsch Informierte zahlen mehr

Eine Innsbrucker Forschergruppe um Rudolf Kerschbamer hat, unterstützt vom Wissenschaftsfonds FWF, eine Feldstudie zur Frage durchgeführt, ob sich Kunden in einem Markt für Vertrauensgüter durch den Erwerb von online verfügbaren Informationen vor Betrügereien durch die Verkäufer schützen können. Als Beispiel diente der Markt für Computerreparaturen.

Related studies

Eco­nomics and Statis­tics (PhD Pro­gram­me)

Doctor of Philosophy

Man­age­ment (PhD Pro­gram­me)

Po­lit­i­cal Science (Doc­toral Pro­gram­me)

So­ci­ol­ogy (Doc­toral Pro­gram­me)

• Birgit Pichler
• Myriam Zollner
• Lorenzo Cafaro/Pixabay
• Michal Jarmoluk auf Pixabay
• Foto: Colourbox.de
• Modell Foto: Colourbox.de

PhD in Economics

PhD students take 16 courses, roughly half of which are spent acquiring the core analytic tools of the profession (microeconomics, macroeconomics, and quantitative methods), with the balance spent applying those tools in particular fields of specialization. All PhD students must complete a doctoral dissertation (thesis).

The PhD in Economics is a STEM designated degree program.

View the complete PhD Rules here

Program Requirements

Doctoral students must complete a minimum of 16 semester courses (64 credits). They are required to successfully complete the core courses by the end of the first year.

Theory and Quantitative Core Requirements

These core courses must be passed by the end of the first year with a grade of at least B- in each course.

• EC 701 Advanced Microeconomics I (4 credits)
• EC 702 Advanced Macroeconomics I (4 credits)
• EC 703 Advanced Microeconomics II (4 credits)
• EC 704 Advanced Macroeconomics II (4 credits)
• EC 707 Advanced Statistics for Economists (4 credits)
• EC 708 Advanced Econometrics I (4 credits)

Students must also take EC 705 Mathematical Economics in the first semester, unless a waiver is granted, and EC 709 Advanced Econometrics II (4 credits) in the third semester.

In addition, students must pass a qualifying examination in both microeconomics and macroeconomics. Students have at most three opportunities to take the qualifying examinations; failing may result in termination from the PhD program.

Field Requirements

All students must pass 2 2-course fields, each with a minimum grade average of B.

In addition, students must take at least 2 other courses. The following fields are generally offered each year:

• Development
• Econometrics
• Economic Theory
• Empirical Finance
• Financial Econometrics
• Industrial Organization
• International Economics
• Labor Economics
• Money/Macroeconomics
• Public Economics

GPA Requirements

All courses must be passed with a grade of B– or higher. An overall grade point average (GPA) of 3.0 must be attained in all courses taken after enrollment in the Graduate School of Arts & Sciences.

Time Requirement

The PhD program is designed so that a typical student can complete all requirements within 5 to 6 years. International students may be subject to additional restrictions imposed by the terms of their visas, as governed by the International Students & Scholars Office (ISSO).

Students are expected to meet the following milestones each year:

By the end of the 1st year:

• Finish and pass all core first-year courses, as well as EC 705 (unless exempted through placement exam).
• Sit for the first attempt at the micro and macro qualifying exams in June. The second attempt, if necessary, is in August.

By the end of the 2nd year:

• Pass EC 709, a required course in Advanced Econometrics.
• Continue and, if possible, complete remaining coursework, including a two-course sequence in each of two fields. A B average (3.0) is required in each of the field course sequence.
• Achieve an overall GPA of at least 3.0.
• If both qualifiers are not passed, the third and final attempt is in June of the second year.
• Each student must prepare a research paper during the second year and the following summer. By April 1 of the second year, the student must ask a faculty member to serve as an advisor on this paper; have this faculty member agree to serve in this manner; and inform the DGS of the topic of the paper and the advisor’s name. The paper is due in the third year as described below.

By the end of the 3rd year:

• Submit the second-year paper by October 1. By October 15, the faculty advisor must provide (i) a grade for the paper; and (ii) a brief written evaluation the paper. These documents will be sent to the DGS and the student. A student must receive a passing grade on the research paper.
• Complete all coursework with GPA of at least 3.0.
• Continue work on research for the dissertation.
• Attend and present at least annually in one of the research workshops until completion of all degree requirements.

Years 4, 5, and (if necessary) 6:

• Student carries out thesis research, defending the thesis no later than the end of the sixth year.

Dissertation

Under the supervision of two faculty advisers, a student prepares a dissertation proposal for presentation at a proposal seminar. If the proposal is approved, the student proceeds to research and write the dissertation. When the dissertation is completed, the student must defend it at a final oral examination. The Graduate School of Arts & Sciences requires that the dissertation be completed within seven years of initial enrollment in the program.

For more details, view the complete PhD Rules here and check out our past PhD Placements here .

M.A. in Economics with a Ph.D. in Statistics

Requirements for Statistics Ph.D. students simultaneously pursuing an M.A. in Economics:

• microeconomics 1 (Econ 501)
• macroeconomics (Econ 502)
• financial economics (Econ 505) or computational economics (Econ 504)
• microeconomics 2 (Econ 508)
• econometrics 2 (Econ 511).
• 5 courses from statistics.
• Courses that are joint listed between the two departments are counted toward the number of courses in the “home department for the particular course.
• Pass qualifier in statistics at the comparable level of a field examination for M.A. in economics.
• Major project – may be directed by statistics faculty but must have strong econometrics content. The doctoral proposal in statistics can count toward this requirement.

Department of Economics, Management and Statistics  DEMS

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• 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

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)

Department of Agricultural and Applied Economics

• Degree Programs

6.0. Doctor of Philosophy Degree Program

The doctoral program in Agricultural and Applied Economics is designed to develop a broad based competence in economic theory and in techniques of quantitative analysis. Dissertation research of students in our department usually addresses applied problems using contemporary economic theory and analytical methods. Students completing our program have demonstrated a high degree of success in academics, business, and government.

Two options are offered for the Doctor of Philosophy in the Agricultural and Applied Economics program. The first option does not require a minor. The second option includes a minor in Family Financial Planning—a joint Ph.D. program between the Department of Agricultural and Applied Economics and the College of Human Sciences. Completion of the Doctoral program in Agricultural and Applied Economics with a minor in Family Financial Planning qualifies graduates to take an exam administered by the Certified Financial Planning Board of Standards to become Certified Financial Planners.

6.1. Prerequisites

Most students will have completed the requirements for an M.S. degree or its equivalent before admission to the doctoral program. If the Master's degree does not meet the prerequisite requirements for entering our Master of Science program (see Section 5.1) then those requirements must be met in order to enter the Ph.D. program. Some exceptional students will be offered the opportunity to pursue a Ph.D. directly from a Bachelor's program. In this case, see the Graduate Coordinator for course and program requirements.

Background in algebra, partial and total differentiation, integration and basic matrix or linear algebra operations are very useful to perform well in coursework and research work.

6.2. Credit Hour Requirements

The doctoral program requires a minimum of 60 credit hours of course work beyond the baccalaureate degree and at least 12 credit hours of dissertation research (AAEC 8000).

6.3. Transfer of Credit

Transfer of graduate credit from other academic institutions may be allowed. The request for transfer must be initiated by the student and supported by the student's committee chair. Transfer decisions are made by the department Graduate Coordinator and must be approved by the Graduate School . Information required in support of transfer requests includes academic transcripts and course catalogue descriptions of each course proposed for transfer. Course syllabi may also be requested to support transfer decisions.

6.4. Substitution for Core Courses

Substitutions for core courses are allowed only under unusual circumstances. Requests for substitutions for core courses must be initiated by the student and his or her committee chair, to the department Graduate Coordinator and must be approved by the Graduate School . Final decisions on substitutions for core courses taught outside our department are made by the department Graduate Coordinator . Decisions on core courses taught in the department are made by the current instructor of the core course being replaced.

6.5. Ph.D. Comprehensive Examination

The purpose of the Ph.D. comprehensive examination is to test the student's ability to integrate knowledge from various subject matter areas and apply appropriate concepts and tools to issues and problems relevant to the discipline. The comprehensive exam is administered by a departmental committee twice each year (usually in May and August) and is normally taken at the end of the first full year of coursework. The exam has two parts which are taken separately. Part 1 of the exam focuses econometric methods and Part 2 covers microeconomic theory. Students have two opportunities to pass both parts of the exam. If both parts of the exam are passed on the first attempt then the student has successfully completed this degree requirement. If one or both parts of the exam are failed on the first attempt (usually in May), the student must retake the failed part(s) at the next offering (in the following August). A second failure of either part of the comprehensive exam will result in dismissal from the student's Ph.D. program.

6.6. Qualifying Examination and Admission to Candidacy

Graduate school rules require that all doctoral students successfully complete a Qualifying Examination for admission to candidacy for the doctor's degree. In the Department of Agricultural and Applied Economics the Dissertation Proposal Defense serves as the Qualifying Exam. Students are allowed two attempts to satisfactorily complete this examination. Failure to satisfactorily complete the examination on a second attempt will result in dismissal from the Ph.D. program. Further information on the Dissertation Proposal and Proposal Defense is provided in Section 9.0 of this Handbook.

6.7. Final Examination

All doctoral candidates are required to pass a public final oral examination which is usually over the general field of the dissertation. Candidates should consult the Graduate School for details regarding scheduling of the final examination. After the final examination, the professor conducting comprehensive exams will send a written notification of the results to the Graduate Secretary for the student's graduate file.

Doctoral candidates, with their major advisor, are required to find their own Dean's Representative for the Graduate School for their defense, preferably someone outside of the department.

Public announcement of exams will be done through the department Graduate Secretary. Exams must be announced at least four weeks prior to the exam date, without exception.

6.9. Degree Program Course Requirements

Listed below are the course requirements for the Ph.D. program options. Courses listed specifically by number are core courses. Note that the option II course requirements have been revised in consultation with the Department of Personal Financial Planning.

Ph.D in Agricultural and Applied Economics - No Minor Required (option1) 1

Course number / course title / credit hours.

• AAEC 5303 / Advanced Production Economics / 3
• AAEC 5307 / Applied Econometrics I / 3
• AAEC 6316 / Advanced International Trade and Policy / 3
• AAEC 5321 / Research Methodology in Economics/ 3
• AAEC 6302 / Food, Ag., and Nat. Resource Policy Analysis / 3
• AAEC 6305 / Economic Optimization / 3
• AAEC 6308 / Advanced Natural Resource Economics / 3
• AAEC 6310 / Demand and Price Analysis / 3
• AAEC 6311 / Applied Econometrics II / 3
• AAEC 6301 / Microeconomic Theory II / 3
• AAEC 6315 / Applied Microeconomics I / 3
• ECO 5311 / Macroeconomic Theory and Policy / 3
• Committee Approved Field Courses / / 24
• AAEC 8000 / Doctor's Dissertation / 12

Total Credit Hours 72

Ph.D. in Agricultural and Applied Economics - Minor in Family Financial Planning (option 2) 1,2

• AAEC 8000 / Doctor's Dissertation / 21
• PFP 5371 / Fundamentals of Personal Financial Planning / 3
• PFP 5372 / Asset Management II / 3
• PFP 5373 / Personal Financial Planning Cpstn. / 3
• PFP 5394 / Retirement Planning / 3
• PFP 5497 / Risk Management and Insurance Planning / 4
• PFP 5398 / Estate Planning / 3
• PFP 5362 / Asset Management I / 3
• PFP 5377 / Client Communication and Counseling / 3
• ACCT 5311 / Individual Study in Accounting / 3

total credit hours 85

1 Numbered courses are core courses. 2 Draft revisions pending review and approval of AAEC faculty.

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70 years after brown v. board of education, new research shows rise in school segregation.

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

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Fariborz Maseeh Department of Mathematics + Statistics

MASEEH DEPARTMENT OF MATHEMATICS + STATISTICS

Graduate Certificate in Applied Statistics

The Graduate Certificate Program in Applied Statistics (GCAS) is primarily designed to provide a companion credential for students in other graduate programs (including Mathematics) who have demonstrated expertise in methods and techniques for the quantitative analysis and modeling of data.

Graduate programs that share a common interest in the application of statistical methods to the analysis of data and the solutions of problems include: Psychology, Civil and Environmental Engineering, Economics, Electrical and Computer Engineering, Computer Science, Engineering and Technology Management, Environmental Sciences and Resources, Mechanical Engineering, Political Sciences, Sociology, Urban Studies, Systems Science.  However, the GCAS program equally serves those who want to pursue just the graduate certificate. 

Program Prerequisites

Prospective students must have a basic preparation in mathematics and statistics and in a particular disciplinary field that would allow for advanced work in statistical methods as well as applications in one or more content areas. This preparation must be demonstrated by the completion of calculus-based courses in probability and distribution theory. A background in basic statistical methodology is assumed. Prerequisites for the GCAS are: 3 terms of calculus (MTH 251-253), linear algebra (MTH 261), and a statistical methods course (STAT 452/552). As the sequence STAT 551,552 is a prerequisite, it is not applicable toward program requirements.

In addition to program prerequisites, applicants must meet the university's minimum admission requirements including English language proficiency.

Application Deadline

This program currently offers rolling admissions for fall, winter, and spring terms. The application closes 2 weeks before the term starts.

For students new to PSU or not currently admitted to a graduate program at PSU

Please refer to the  Application Instructions  if you are not currently admitted to another graduate program at PSU.

For current graduate students at PSU

If you wish to add the certificate to your current degree program, please complete the online  GO-19 Request for Change of Major form and email unofficial transcripts (showing you've met the program prerequisites) to the graduate program administrator in the Mathematics + Statistics Department at [email protected].

Cost and Funding

Refer to the University Student Finance web page for tuition and financial aid information.  

Graduate certificate programs are ineligible for a graduate teaching assistantship.

Program Goals, Objectives

Many graduate programs include a research methods component that requires the student to acquire some exposure to statistical methods as the basis for the design of experiments and analysis of data. The Graduate Certificate in Applied Statistics (GCAS) goes well beyond those requirements -- the student develops both a depth of understanding of methods and a breadth of application across disciplines. It is expected that a student who earns this certificate would be capable of performing sophisticated statistical analysis and modeling for problems within his or her particular discipline. They would also be expected to be able to access and understand consultation with professional statisticians and provide consultation in the application of statistical methods for research purposes and in the solution of practical problems. The goal of the GCAS program is a coordinated plan for which students will be assured of exposure to statistical techniques needed in most applications.

Course Requirements

This Graduate Certificate credential may be completed with a minimum of 24 credit hours of statistical graduate coursework with no comprehensive exam, while the MS in Statistics requires more extensive coursework and examinations. The certificate requires the successful completion of course work distributed as three components.

Applied statistics core sequence (9 credits)

The goal of this sequence is to introduce students to fundamentals of applied statistics.  The three-term core course sequence: STAT 564 Applied Regression Analysis (3 credits)  STAT 565 Experimental Design: Theory and Methods I (3 credits) STAT 566 Experimental Design: Theory and Methods II (3 credits)

Statistical consulting (3 credits)

STAT 570 Statistical Consulting (3 credits) provides experience in dealing with real statistical problems.  Please note that this course is only offered during spring term.

Additional applied statistics courses (minimum 12 credits)

The objective is developing a breadth of knowledge in the application of statistical methods within the discipline and in related areas. A minimum of 12 additional hours chosen from the list of theory and interdisciplinary courses below. Please note that 510/610 courses and STAT 551, 552 are not acceptable toward the certificate.

Students are encouraged to meet with their adviser at the beginning of their program and consult with them regularly throughout their program. Please contact the GCAS program adviser during the term prior to the term of anticipated graduation to confirm that all program requirements have been completed. Instructions for applying to graduate as well as graduate deadlines can be found on the Graduate School web site .

A student must have a minimum 3.00 GPA on the courses applied to the program of study, as well as a minimum 3.00 GPA in all graduate-level courses taken at PSU. Although grades of C+, C, and C- are below the graduate standard, they may be counted as credit toward a graduate certificate with the specific written approval of the department if taken at PSU after the term of formal admission to the graduate program.

Graduate Policies and Procedures

Students are responsible for knowing University-level graduate policies and procedures for obtaining the degree. These policies and procedures are in Graduate School section of the  University Bulletin . Several of the most frequently asked about University-level graduate policies and procedures can also be found on the  Graduate School web site .

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MIT’s Master of Applied Science in Data, Economics, and Design of Policy program adds a public policy track

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MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) and Department of Economics have announced an expansion of their jointly administered Master of Applied Science in Data, Economics, and Design of Policy (DEDP) program . This expansion adds a new public policy track to complement the existing international development track, opening up new avenues for student learning and research. 

Designed to tackle poverty alleviation and other pressing policy challenges in the United States and other high-income countries, the curriculum of the new track spans a diverse set of issues, from domestic concerns like minimum wage and consumer welfare to global matters including trade, climate change, and immigration. Applications for the public policy track will open this fall, with the inaugural cohort set to arrive on MIT’s campus in spring 2026.

The DEDP program, led by MIT professors and Nobel laureates Abhijit Banerjee and Esther Duflo, along with professors Sara Fisher Ellison and Benjamin Olken, was established with the mission of equipping diverse cohorts of talented professionals with the knowledge and skills to tackle poverty using evidence-based approaches. The new master’s degree track will support this mission while also underscoring the program’s commitment to addressing a broad array of critical challenges in the fight against poverty worldwide.

"The DEDP program has proven successful on many dimensions, and we are enthusiastic about leveraging its successes to address a broader set of social challenges,” says Ellison, a faculty lead for the program. “The public policy track will enable us to apply evidence-based methodology to poverty alleviation and other related issues in the context of high-income countries, as well. Given increasing levels of wealth and income inequality in these countries, we feel that the timing is opportune and the need is great."

The DEDP program distinguishes itself with an innovative admissions model that prioritizes demonstrated ability and motivation over traditional credentials, such as standardized tests and recommendation letters. To be eligible to apply to the master’s program, candidates must have earned a DEDP MicroMasters credential by passing five of the DEDP online courses. The courses are completely free to audit. Those who wish to earn a course certificate can pay a fee, which varies by the learner’s ability to pay, to take the proctored exam. While applications are reviewed holistically, performance in these classes is the primary factor in admissions decisions.

This approach democratizes access to higher education, enabling students from typically underrepresented backgrounds to demonstrate their potential for success. Notably, the program has welcomed many students from nontraditional backgrounds, such as a student who enrolled directly from high school (and who is now a second-year PhD student in economics at MIT), reflecting the ambition of its faculty directors to make higher education more accessible.

Sofia Martinez, a graduate of the class of 2023 and now co-founder of Learning Alliance , says, "Without the MicroMasters paving the way, applying to MIT or any similar institution would have been unthinkable for us. Initially, my aim in taking the online courses wasn't to pursue the residential program; it was only after witnessing my own progress that I realized the possibility wasn't so distant after all. This sentiment resonates with many in our cohort, which is truly humbling.”

Since its launch in 2020, the DEDP master’s program has conferred degrees to 87 students from 44 countries, showcasing its global reach and the success of its admissions model. Upon arriving on campus, students embark on an accelerated master's program. They complete a full course load in the spring, followed by a capstone project in the summer, applying the theoretical knowledge and practical skills gained through the program at research and policy organizations.

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RIT graduate pursues Ph.D. across time zones

Nastaran Nagshineh, center, defended her Ph.D. thesis at RIT in April. Faculty from RIT’s Rochester and Dubai campuses served on her thesis committee and include, from left to right, Kathleen Lamkin-Kennard, Steven Weinstein, Nathaniel Barlow, and David Kofke (a professor at the University at Buffalo). Mohamed Samaha participated remotely and appears on the video screen behind the group and alongside Nagshineh’s picture.

Nastaran Nagshineh is one of the first Ph.D. candidates to bridge RIT’s Rochester and Dubai campuses. Her accomplishment creates a path for future students at the university’s international campuses.

Nagshineh completed her Ph.D. in mathematical modeling while working full time as a mathematics lecturer at RIT Dubai in the United Arab Emirates, teaching as many as five classes a semester. She described her Ph.D. journey as “an exercise in perseverance” due to competing demands and long days. Rochester is eight hours behind Dubai, and the time difference meant many late-night classes and meetings.

“I saw this collaboration as an opportunity, rather than as a challenge, because my primary adviser, Dr. Steven Weinstein (RIT professor of chemical engineering), and my co-adviser, Dr. Mohamed Samaha (RIT Dubai associate professor of mechanical engineering), both have the same area of research interest,” she said. “They both worked toward my success.”

Nagshineh is one of 67 RIT Ph.D. students who defended their thesis this academic year and who will earn their doctorate. RIT awarded 63 Ph.D. degrees in 2023.

In 2020-2021, RIT’s Graduate School met and surpassed the university’s goal of conferring 50 Ph.D. degrees during an academic year. That number will continue to grow as students cycle through the seven new Ph.D. programs that RIT has added since 2017, said Diane Slusarski , dean of RIT’s Graduate School.

Meeting these goals puts RIT on a path toward achieving an “R1,” or research-intensive designation, from the Carnegie Classification of Institutions of Higher Learning. RIT is currently ranked as an R2 institution . Many factors go into changing a university’s status, including research investment and maintaining a three-year average of 70 Ph.D. degrees awarded per year, according to Slusarski.

“We have met the goals of the strategic plan, and now we look forward to contributing to the research innovation in the future,” Slusarski said. “We want to help the new programs thrive and win national research awards.”

RIT’s emphasis on high-level research is seen in Nagshineh’s Ph.D. work. She applies mathematical modeling to the field of fluid dynamics. Her research has been published in top-tier journals and has gained notice, said Weinstein, her thesis adviser.

Weinstein describes Nagshineh’s accomplishments as “a testament to a fantastic work ethic and commitment” and is inspirational to younger students at Rochester and Dubai.

“The collaboration between RIT Dubai/Rochester has continued,” he said. “Another paper was submitted a few weeks ago with Mohamed Samaha and Nate Barlow (RIT associate professor in the School of Mathematics and Statistics) as co-authors, as well as Cade Reinberger, a younger Ph.D. student in my research group.”

Mathematical modeling is one of RIT’s newer Ph.D. degree programs, and Nagshineh is among its earliest graduates. The program has doubled in size since it began accepting students in 2017, Slusarski said. This past fall, the mathematical modeling program had 35 students, with two graduating this year.

Altogether, RIT has 13 Ph.D. degree programs currently enrolling 438 students, with computing and information sciences accounting for the largest with 117 students. RIT’s other Ph.D. programs include astrophysical sciences and technology , biomedical and chemical engineering , business administration , color science , electrical and computer engineering, imaging science , mechanical and industrial engineering , microsystems engineering , and sustainability .

New programs in cognitive science and physics will launch in the fall.

The growth in RIT graduate education—with more than 3,000 master’s and doctoral students—reflects a demographic change in the student population, Slusarski said. “We have a higher percentage of women in the graduate programs than we have for RIT undergraduate programs.”

RIT’s graduate programs enroll 42 percent women, according to Christie Leone , assistant dean for the Graduate School.

Nagshineh, who also holds an MS in electrical engineering from RIT Dubai, welcomes her role as a mentor to other women students on both campuses.

“As a young woman in an Arabic country, the power of women is often underestimated and undervalued, and I hope to serve as a role model to female students, especially those that question their path,” Nagshineh said.

She plans to continue in her career as a professor and a researcher. “I would like to pursue a research program where I can advise my own students and teach them more deeply.”

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