PhD in Economics, Statistics and Data Science

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

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.

Governance

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.

Exams

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. These papers have to be 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.

Seminars

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 in 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.

Placement

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
N. Surname Name University Department Curriculum
1 ALBONICO Alice Milano-Bicocca DEMS ECO
2 ARGIENTO Raffaele Bergamo Statistics STAT
3 ATHANASOGLOU Stergios Milano-Bicocca DEMS ECO
4 BAILLIE Richard King's College London-UK Economics ECO
5 BEN-PORATH Elchanan Hebrew-Israel Economics ECO
6 BERTA Paolo Milano-Bicocca Statistics and Quantitative Methods BiDAB
7 BERTOLETTI Paolo Milano-Bicocca DEMS BiDAB
8 BINELLI Chiara Bologna Economics BiDAB
9 BOLLINO Carlo Andrea Perugia Economics ECO
10 BRENDAN Murphy UCD- Ireland Mathematics and Statistics BiDAB
11 CAMERLENGHI Federico Milano-Bicocca DEMS STAT
12 CASTELLETTI Federico Milano-Catholic Statistics BiDAB
13 COLCIAGO Andrea Milano-Bicocca DEMS ECO
14 CONSONNI Guido Milano-Catholic Statistics STAT
15 CRETI' Anna Paris-Dauphine-France Géopolitique de l'Energie et des Matières Premières ECO
16 DALLA PELLEGRINA Lucia Milano-Bicocca DEMS ECO
17 D'AMBROSIO Conchita Luxembourg-Luxembourg Lettres, Sciences Humaines, Arts et Sciences de l'Education ECO
18 DIA Enzo Milano-Bicocca DEMS ECO
19 FARAVELLI Marco Queensland-Australia Economics ECO
20 FERRARIS Leo Milano-Bicocca DEMS ECO
21 GATTAI Valeria Milano-Bicocca DEMS ECO
22 GRESELIN Francesca Milano-Bicocca Statistics and Quantitative Methods STAT
23 GUERZONI Marco Milano-Bicocca DEMS BiDAB
24 GUINDANI Michele California Irvine-US Statistics BiDAB
25 HECQ Alain Maastricht-The Netherlands Economics BiDAB
26 LOVAGLIO Pietro Giorgio Milano-Bicocca Statistics and Quantitative Methods STAT
27 LUNARDON Nicola Milano-Bicocca DEMS STAT
28 MANERA Matteo Milano-Bicocca DEMS BiDAB
29 MANTOVANI Marco Milano-Bicocca DEMS ECO
30 MARCHESI Silvia Milano-Bicocca DEMS ECO
31 MCLACHLAN Geoffrey Queensland-Australia Mathematics STAT
32 MENDOLA Mariapia Milano-Bicocca DEMS ECO
33 MERCORIO Fabio Milano-Bicocca Statistics and Quantitative Methods BiDAB
34 MICHELANGELI Alessandra Milano-Bicocca DEMS ECO
35 MIGLIORATI Sonia Milano-Bicocca DEMS STAT
36 MORANA Claudio Milano-Bicocca DEMS ECO
37 MOSCONE Francesco Brunel London-UK Environment, Health and Societies STAT
38 NAIMZADA Ahmad Milano-Bicocca DEMS BiDAB
39 NIPOTI Bernardo Milano-Bicocca DEMS STAT
40 ONGARO Andrea Milano-Bicocca DEMS STAT
41 PACI Lucia Milano-Catholic Statistics BiDAB
42 PELAGATTI Matteo Milano-Bicocca DEMS BiDAB
43 PELUSO Stefano Milano-Bicocca Statistics and Quantitative Methods BiDAB
44 PENNONI Fulvia Milano-Bicocca Statistics and Quantitative Methods STAT
45 PIEVATOLO Antonio Milano-National Research Council (CNR)  Institute for Applied Mathematics and Information Technologies BiDAB
46 PINI Alessia Milano-Catholic Statistics BiDAB
47 PORCU Emilio

Khalifa University of Science and Technology - United Arab Emirates

Mathematics, Statistics and Physics STAT
48 QUATTO Piero Milano-Bicocca DEMS STAT
49 RIANI Marco Parma Economics and Business STAT
50 SANTORO Emiliano Copenhagen-Denmark Economics ECO
51 SCHARFSTEIN Daniel Johns Hopkins-US Bloomberg School of Public Health STAT
52 SOLARI Aldo Milano-Bicocca DEMS STAT
53 STANCA Luca Milano-Bicocca DEMS ECO
54 TAMBURRI Damian Eindhoven-The Netherlands Computer Science BiDAB
55 VITTADINI Giorgio Milano-Bicocca Statistics and Quantitative Methods STAT
56 ZITIKIS Ricardas Western-Canada Statistics and Actuarial Sciences STAT

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.

Events 2018-2022

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 1st CefES International Conference on European Studies, to be held at the University of Milano-Bicocca, Building U6, on June 10th-11th 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-30th 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 ChangeTwo plenary sessions on the economic and financial implications of climatic change have been organized on June 22nd 2018, following Prof. Robert Engle’s talk, from 11.30am to 4.45pm, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

XXXV cycle - Teaching activities - Year I - courses/timetable

I Term

The I term teaching activities start on 4 November 2019 and end on 20 December 2019. The I term exam session starts on 7 January 2020 and ends on 10 January 2020.

The timetable of the I term courses is available here.

The courses/modules offered during the I term for the curriculm ECO are:

- Computational Statistics I (Instructor: Prof. Bertarelli)

Mathematics I (Instructor: Prof. Pireddu)

Mathematics II (Instructor: Prof. Cavalli)

Mathematics – Linear algebra (Instructor: Prof. Pecora)

Mathematics III (Instructor: Prof. Longo)

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

Mathematical Analysis (modules I, II and III; Instructors: Proff. ZancoDe BernardiMiglierina)

Numerical Optimization (Instructor: Prof. Dassi)

 

II Term

The II term teaching activities start on 13 January 2020 and end on 27 March 2020.  The II term exam session starts on 30 March 2020 and ends on 9 April 2020.

The courses/modules offered during the II term for the curriculm ECO are:

Econometrics I – The Linear Regression Model (Instructor: Prof. Manera)

Econometrics II – Microeconometrics (Instructor: Prof. Mancusi)

- Econometrics III – Time Series Part I & II (in common with curriculum STAT; Instructor: Dr. Bastianin)

- Microeconomics I – Consumption, Production, Market Forms  (Instructor: Prof. Athanasoglou)

- Microeconomics II – Game Theory (Instructor: Prof. Gilli)

- Microeconomics III – Contract Theory (Instructor: Prof. Colombo)

- Microeconomics IV – (Instructor: Prof. Bertoletti)

The courses/modules offered during the II term for the curriculm STAT are:

Probability I (Instructor: Prof. Camerlenghi)

Probability II (Instructor: Prof. Camerlenghi)

Stochastic Processes (Instructor: Prof. Davydov)

Computational Statistics II (in common with curriculum ECO – Instructor: Dr.ssa Pini)

Statistical Inference I (Instructor: Prof. Lunardon)

- Econometrics III – Time Series Part I & II (in common with curriculum ECO; Instructor: Dr. Bastianin)

 

III Term

The timetable of the III term courses is available here.

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

-  Macroeconomics I –  (Instructor: Prof. Femminis)

-  Macroeconomics III –  (Instructor: Prof. Tirelli)

-  Macroeconomics III – (Instructor: Dr.ssa Albonico)

-  Macroeconomics II –  (Instructor: Prof. Ropele)

-  Macroeconomics IV – (Instructor: Dr.ssa Barbaro)

Research Methods – (Instructor: Prof. Colussi)

- Finance I – Empirical Corporate Finance (Instructor: Prof. Signori)

- Finance II – Asset Pricing Theory (Instructor: Prof. Sbuelz)

- Finance III – Banking (Instructor: Prof. Cerasi)

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

- Statistical Inference II (Instructor: Prof. Solari)

Statistical Inference III (Instructor: Prof. Dolera)

- Bayesian Statistics I (Instructor: Prof. Ntzoufras)

- Bayesian Statistics II (Instructor: Prof. Argiento)

- R for Data Science – (Instructor: Prof. Melloncelli)

- Data Management (Instructor: Prof. Maurino)

Some of the courses are offered jointly with the PhD program in Economics and Finance of the Catholic University of Milan (UCSC). For this reason, some lectures will be held at the premises of UCSC, as clearly indicated in the timetable.

 

IV Term

The IV term teaching activities start beginning of September 2020.  The IV term exam session will be fixed in due course. 

The timetable of the IV term courses for the curriculum STAT is available here.

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

- Statistical Modelling (Instructors: Prof.ssa F. Greselin, Prof. Verzillo, Prof.ssa F. Pennoni, F. Bartolucci) 

- Statistical Learning I & II (Instructor: Prof. Rajen Shah)

- Statistical Learning III (Instructor: Prof. Aldo Solari)

XXXV cycle - Teaching activities - Year II - courses/timetable

Courses (reading groups) offered in year 2020-21 for the curriculum ECO:                             

Reading groups in Advanced Macroeconomics                                                         

Instructor                                                 Title                                                                              Term

Andrea Colciago - Rajssa Mechelli            Heterogeneous Agents Models in Macroeconomics         I

Domenico Delli Gatti                                 Financial Frictions                                                          II

Silvia Marchesi                                          The Economics of Sovereign Debt                                   I

Domenico Massaro                                    Behavioral and Experimental Macroeconomics               II

Patrizio Tirelli                                            Unconventional Monetary Policies                                  II

Reading groups in Advanced Microeconomics                  

Istructor                                                     Title                                                                             Term

Gianluca Cassese                                        Fixed Point Techniques in Equilibrium Analysis             II

Stefano Colombo                                        Spatial Models in Microeconomics                                 II

Valeria Gattai                                              International Economics                                                 I

Fabrizio Panebianco                                    Social Network Theory                                                   I

Reading groups in Advanced Econometrics                        

Instructor                                                     Title                                                                            Term

Andrea Bastianin                                        Economic Forecasting                                                     II

Andrea Monticini                                        Bootstrap Methods for Time Series                                 II

Gazi Salah Uddin                                        Advanced Time Series Modelling                                     I

Reading groups in Advanced Finance                    

Instructor                                                     Title                                                                           Term

Elena Beccalli                                              Empirical Banking                                                          I

Ettore Croci                                                Advanced Corporate Finance                                          I

Andrea Tarelli                                             Advanced Asset Pricing and Portfolio Management        II

Reading groups in Advanced Computational Statistics and Coding                         

Instructor                                                     Title                                                                             Term

Alice Albonico                                            Estimated DSGE models                                                   II

Domenico Massaro                                    Computational Frameworks in Macroeconomics               II

 

The timetable of the reading groups for the curriculum ECO offered during term I can be found here.

The timetable of the reading groups for the curriculum ECO offered during term II can be found here

 

Courses (reading groups) offered in year 2020-21 for the curriculum STAT:

Instructor                                                  Title                                                             Term 

Paci Lucia                        Statistics for Spatio-Temporal Data                                          I

Camerlenghi – Nipoti       Bayesian Non parametric Mixture Models                                 II

Borgoni Riccardo             Some issues in Statistical Modelling                                         II

Borrotti Matteo                Introduction to Deep Learning                                             II-III

Candelieri Antonio           Automated Machine Learning&Neural Architecture Search       III

 

The timetable of the reading groups for the curriculum STAT is available here

XXXVI cycle - Teaching activities - Year I (terms I-II-III-IV) - courses/timetable

I Term

The I term teaching activities start on 26 October 2020 and end on 23 December 2020. The I term exam session starts on 7 January 2021 and ends on 13 January 2021. 

The timetable of the I term courses is available here.

For term I lessons will be online in live mode. Students will be contacted by the professors for more details.

 

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

- Computational Statistics I (Instructor: Prof. Bertarelli)

- Mathematics – Linear algebra (Instructor: Prof. Pecora)

Mathematics I (Instructor: Prof. Pireddu);

Mathematics II (Instructor: Prof. Cavalli);

Mathematics III (Instructor: Prof. Longo)

 

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

Mathematical Analysis (modules I, II and III; Instructors: Proff. ZancoDe BernardiMiglierina)

- Numerical Optimization (Instructor: Prof. Dassi

 

II Term

The II term teaching activities start on 11 January 2021 and end on 31 March 2021.  The II term exam session starts on 6 April 2021 and ends on 9 April 2021. 

For term II the lessons will be online in live mode.  Students will be contacted by the professors for more details.

The return to live lessons will be communicated promptly, as soon as the pandemic conditions allow it.

The timetable of the II term courses is available here.

 

The courses/modules offered during term II for the curriculm ECO are:

Econometrics I – The Linear Regression Model (Instructor: Prof. Manera)

Econometrics II – Microeconometrics (Instructor: Prof. Mancusi);

Econometrics III – Time Series Part I & Part II (Instructor: Prof. Bastianin; Part II  in common with curriculum STAT)

Microeconomics I – Consumption, Production, Market Forms  (Instructor: Prof. Athanasoglou)

Microeconomics II – Game Theory (Instructor: Prof. Gilli)

Microeconomics III – Contract Theory (Instructor: Prof. Colombo)

- Microeconomics IV – (Instructor: Prof. Bertoletti)

 

The courses/modules offered during term II for the curriculm STAT are:

Probability I & II (Instructor: Prof. Camerlenghi)

Stochastic Processes (Instructor: Prof. Buonaguidi)

- R for Data Science (Instructor: Prof. Melloncelli)

- Statistical Inference I (Instructor: Prof. Lunardon)

Econometrics III – Time Series – Part II (Instructor: Prof. Bastianin

 

III Term

The III term teaching activities start on 12 April 2021 and end on 2 July 2021.  The III term exam session starts on 12 July 2021 and ends on 23 July 2021. 

For term III the lessons will be online in live mode.  Students will be contacted by the professors for more details.

The return to live lessons will be communicated promptly, as soon as the pandemic conditions allow it.

The timetable of term III courses is available here.

 

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

Macroeconomics part I –  (Instructor: Prof. Femminis)

-  Macroeconomics part II –  (Instructor: Prof. Tirelli);

Macroeconomics part II – (Instructor: Dr.ssa Albonico);

-  Macroeconomics part II –  (Instructor: Prof. Ropele);

-  Macroeconomics part II – (Instructor: Dr.ssa Barbaro)

Research Methods – (Instructor: Prof. ColussiDr.ssa Villar)

Computational Statistics II – Part I (Instructor: Prof. Pini)

-  Finance I – Empirical Corporate Finance (Instructor: Prof. Signori)

-  Finance II – Asset Pricing Theory (Instructor: Prof. Sbuelz)

Finance III – Banking (Instructor: Prof. Cerasi)

 

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

- Statistical Inference II (Instructor: Prof. Solari)

- Statistical Inference III (Instructor: Prof. Dolera)

- Bayesian Statistics I (Instructor: Prof. Nipoti)

- Bayesian Statistics II (Instructor: Prof. Argiento)

- Computational Statistics II – Part I (Instructor: Prof. Pini)

- Computational Statistics II – Part II (Instructor: Prof. Rigon)

- Data Management (Instructor: Prof. Maurino)

 

IV Term

The IV term teaching activities start beginning of September 2021.  The IV term exam session will be fixed in due course.

The timetable of the IV term courses for the curriculum STAT is available here.

 

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

- Statistical Modelling part I (Instructor: Prof. Castelletti) 

- Statistical Modelling part II (Instructor: Prof.ssa F. Greselin

- Statistical Modelling part III (Instructor: Prof.ssa F. Pennoni

- Statistical Modelling part IV (Instructor: Prof. F. Bartolucci) 

- Statistical Learning part I-II (Instructor: Prof. O. Papaspiliopoulos – details in the paragraph ‘Events 2021’)

- Statistical Learning part III (Instructor: Prof. A. Solari)

XXXVI cycle - Teaching activities - Year II - courses/timetable

Courses (Reading Groups) offered in year 2021/22 for the curriculum ECO:

I term:

Alice Albonico

Estimated DSGE models

Silvia Marchesi

Sovereign Debt

Sara Giunti / Giulia Tura

Economics of Immigration

Elena Villar / Daria Vigani

Empirical Methods in Health Economics: Ageing

Elena Beccalli

Empirical Banking

Ettore Croci

Corporate Finance

 

II term:

Domenico Delli Gatti

Financial Frictions

Valeria Gattai

International Economics

Stefano Colombo

Spatial Economics

Mario Gilli

Global Games: Theory and Applications

Andrea Bastianin

Economic Forecasting

Francesca Pampurini

Efficiency Valuation

Andrea Tarelli

Advanced Asset Pricing and Portfolio Management

 

Courses (Reading Groups) offered in year 2021/22 for the curriculum STAT:

 

prof.ssa Paci

 Statistics for spatio-temporal data

periodo mag

prof. Borgoni

 Some issues in Stat Modelling

periodo mar-apr

prof. Borrotti

 Deep learning

periodo mar

proff. Camerlenghi/Nipoti

 Bayesian Non parametric Mixture Models

periodo dic

prof. Candelieri

 Automated Machine Learning&Neural Architecture Search

periodo feb

XXXVII cycle - Teaching activities - Year I (term I - II - III - IV) - courses/timetable

 

I Term

The I term teaching activities start on 25 October 2021 and end on 23 December 2021. The I term exam session starts on 7 January 2022 and ends on 14 January 2022. 

The timetable of the I term courses is available here

Lessons will be in presence

 

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

- Computational Statistics I (Instructor: Prof. G. Bertarelli)

- Mathematics – Linear algebra (Instructor: Prof. N. Pecora)

- Mathematics I (Instructor: Prof. M. Pireddu);

- Mathematics II (Instructor: Prof. F. Cavalli);

- Mathematics III (Instructor: Prof. M. Longo)

 

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

- Mathematical Analysis (modules I, II and III; Instructors: Proff. C. Zanco, C.A. De BernardiE. Miglierina)

- Numerical Optimization (Instructor: Prof. F. Dassi

 

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

- Programming in Python (Instructor: prof. M. Cesarini)

- Architecture for Big Data Processing (Instructor: prof. V. Moscato)

- Architecture for Big Data Processing Lab (Instructor: prof. G. Sperli)

 

II Term

The II term teaching activities start on 17 January 2022 and end on 8 April 2022.  The II term exam session starts on 18 April 2022 and ends on 22 April 2022.

The timetable of the II term courses is available here.

 

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

- Econometrics I – The Linear Regression Model (Instructor: Prof. M. Manera)

- Econometrics II – Microeconometrics (Instructor: Prof. M.L. Mancusi);

- Econometrics III – Time Series Part I & Part II (Instructor: Prof. A. Bastianin; Part II  in common with curriculum STAT)

- Microeconomics I – Consumption, Production, Market Forms  (Instructor: Prof. S. Athanasoglou)

- Microeconomics II – Game Theory (Instructor: Prof. M. Gilli)

- Microeconomics III – Contract Theory (Instructor: Prof. L. Colombo)

- Microeconomics IV – (Instructor: Prof. P. Bertoletti)

 

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

- Probability I & II (Instructor: Prof. F. Camerlenghi)

- Stochastic Processes (Instructor: Prof. B. Buonaguidi)

- R for Data Science (Instructor: Prof. A. Melloncelli)

- Statistical Inference I (Instructor: Prof. M. Cattelan)

- Econometrics III – Time Series – Part II (Instructor: Prof. A. Bastianin)

 

The courses/modules offered during term II for the curriculum BiDAB are:

- Probability (Instructor: Prof. A.M. Di Brisco)

- Big Data Processing Lab (Hadoop/Spark) (Instructor: Prof. D. Tamburri)

- Statistical Inference I (Instructor: Prof. R. Ascari)

- Statistical Inference II (Instructor: Prof. R. Corradin)

 

III Term

The III term teaching activities start on 26 April 2022 and end on 8 July 2022.  The III term exam session starts on 18 July 2022 and ends on 28 July 2022. 

For term III the lessons will be in presence.

The timetable of term III courses is available here.

 

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

Macroeconomics I –  (Instructor: Prof. G. Femminis)

-  Macroeconomics II –  (Instructor: Prof. A. Gobbi);

-  Macroeconomics III –  (Instructor: Prof. T. Ropele);

-  Macroeconomics - Tutorials – (Instructor: Dr. D. Barbaro)

Research Methods – (Instructor: Prof. T. Colussi – Dr. E. Villar)

-  Computational Statistics II – Part I (Instructor: Prof. A. Pini)

-  Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori)

-  Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz)

-  Finance III – Banking (Instructor: Dr. M. Migliavacca)

 

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

- Statistical Inference II (Instructor: Prof. A. Solari)

- Statistical Inference III (Instructor: Prof. E. Dolera)

- Bayesian Statistics I (Instructor: Prof. B. Nipoti)

- Bayesian Statistics II (Instructor: Prof. R. Argiento)

- Computational Statistics II – Part I (Instructor: Prof. A. Pini)

- Computational Statistics II – Part II (Instructor: Prof. T. Rigon)

- Data Management (Instructor: Prof. A. Maurino)

 

The courses/modules offered during term III for the curriculum BIDAB are:

- Technology and Innovation Management

   * The sources of innovation and the strategic management of intellectual property (Instructor: Prof. S. Torrisi)

   * Collaborative innovation and internationalization of R&D (Instructor: Prof. L. D'Agostino)

   * The financing of innovation (Instructor: Prof. F. Di Pietro)

   * Technology diffusion, firms’ survival and knowledge Sharing (Instructor: Prof. M. Guerzoni)

- Natural Language Understandings (Instructor: Prof. E. Cambria)

- Social Media Analytics (Instructor: Prof. R. Boselli)

- Machine Learning (Instructor: Prof. L. Malandri)

 

IV Term

 

The IV term teaching activities start beginning of September 2022.  The IV term exam session will be fixed in due course.

The timetable of the IV term courses for the curricula STAT and BIDAB are available here.

 

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

 

- Statistical Modelling I (Instructor: Prof. F. Castelletti) 

- Statistical Modelling II (Instructor: Prof.ssa F. Greselin

- Statistical Modelling III (Instructor: Prof. F. Bartolucci

- Statistical Modelling IV (Instructor: Prof. F. Bartolucci) 

- Statistical Learning I-II (Instructor: Prof. B. Szabo)

Statistical Learning III (Instructor: Prof. A. Solari)

The courses/modules offered during term IV for the curriculum BIDAB are:

- Statistical Modelling I (Instructor: Prof. F. Castelletti) 

- eXplainable AI for Business Value (Instructor: Prof. F. Mercorio)

- Deep Learning and Computer Vision for Business (Instructor: Prof. E. Frontoni)

XXXVII cycle - Teaching activities - Year II - courses/timetable

 

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)

- Applications of Game Theory (Instructor: Prof. M. Gilli)

- Empirical Banking (Instructor: Prof. Elena Beccalli)

- Advanced Asset Pricing and Portfolio Management (Instructor: Prof. A. Tarelli)

- Empirical Corporate Finance (Instructor: Prof. E. Croci)

- Programming in Python (Instructor: Prof. L. Viarengo)

II term (January 2023 – April 2023)

- Spatial Economics (Instructor: Prof. S. Colombo)

- Financial Frictions (Instructor: Prof. D. Delli Gatti)

- The Microeconomics of International Trade (Instructor: Prof. V. Gattai)

- Innovation and Industrial Evolution (Instructor: Prof. C. Garavaglia)

- Structural VAR Models (Instructors: Proff. V. Colombo, G. Rivolta)

- Applied Health Economics and Policy (Instructors: Proff. G. Turati, E. Cottina, L. Salmasi)

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)

- Advanced Macroeconomics (Instructors: TBC)

Note:the timetable of the extra-RG is under construction and will be 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)

- Bayesian Non-parametric Mixed Models (Instructors: Proff. F. Camerlenghi, B. Nipoti)

II term (January 2023 – April 2023)

- Some Issues in Statistical Modelling (Instructor: Prof. R. Borgoni)

- Empirical Bayes in Bayesian Inference (instructor: Prof. S. Rizzelli)

- Automated Machine learning & Neural Architectural Search (Instructor: Prof. A. Candelieri)

- Deep Learning (Instructor: Prof. M. Borrotti)

Note: the timetable (under construction) 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 (Instructor: Prof. F. Mercorio)

- Human-centered AI (Instructor: Prof. M. Zanzotto)

Note: the timetable of the RG for the curriculum BIDAB is under construction and will be available here. 

XXXVIII cycle - Teaching activities - Year I (term I - II - III - IV) - courses/timetable

 

I Term

 

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)

- Mathematics – Linear algebra (Instructor: Prof. N. Pecora)

Mathematics I (Instructor: Prof. D. Visetti);

Mathematics II (Instructor: Prof. F. Cavalli);

Mathematics III (Instructor: Prof. M. Longo)

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

Mathematical Analysis (modules I, II and III; Instructors: Proff. C. Zanco, C.A. De Bernardi, E. Miglierina)

Numerical Optimization (Instructor: Prof. L. Mascotto) 

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

Programming in Python (Instructor: Prof. M. Cesarini)

- Architecture for Big Data Processing (Instructor: Prof. V. Moscato)

- Architecture for Big Data Processing Lab (Instructor: Prof. G. Sperlì)

 

 

II Term

 

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. 

Note: the timetable of the II term courses is currently under construction and will be available here.

 

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

- Econometrics I (Instructor: Prof. M. Manera)

- Econometrics I – tutorials (Instructor: TBC)

- Econometrics II (Instructor: Prof. M.L. Mancusi)

- Econometrics II – Tutorials (Instructor: TBC)

- Econometrics III (Instructor: Prof. A. Ugolini)

- Econometrics III- Tutorials (Instructor: Dr. D. Valenti)

- Microeconomics I (Instructor: Prof. M. Mantovani)

- Microeconomics I – Tutorials (Instructor: Dr. F. Campo)

- Microeconomics II (Instructtor: Prof. M. Gilli)

- Micoreconomics II – Tutorials (Instructor: Prof. M. Gilli)

- Microeconomics III (Instructor: Prof. L. Colombo)

- Microeconomics III – Tutorials (Instructor: TBC)

- Micoreconomics IV (Instructor: Prof. P. Bertoletti)

- Microeconomics IV – Tutorials (Instructor: Dr. G. Crea)

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

- Probability I (Instructor: Prof. F. Camerlenghi)

- Probability II (Instructor: Prof. F. Camerlenghi)

- Stochastic Processes (Instructor: Prof. B. Buonaguidi)

- R for Data Science (Instructor: TBC)

- Statistical Inference I (Instructor: TBC)

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

- Probability (Instructor: Prof. TBC)

- Statistical Inference I (Instructor: Prof. R. Ascari)

- Statistical Inference II (Instructor: TBC)

- Big Data Processing Lab (Hadoop/Spark) (Instructor: TBC)

 

III Term

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 28 July 2023. 

Note: the timetable of the III term courses is currently under construction and will be available here.

 

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

Mandatory

- Macroeconomics I (Instructor: Prof. G. Femminis)

- Macroeconomics II (Instructor: Prof. A. Albonico)

- Macroeconomics III (Instructor: Prof. T. Ropele)

- Macroeconomics IV (Instructor: Dr. B. Barbaro)

- Computational Statistics II (a) (Instructor: TBC)

- Research Methods (Instructor: Prof. T. Colussi)

- Research Methods – Tutorials (Instructor: Dr. E. Villar)

 Optional

- Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori)

- Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz)

- Finance III – Banking (Instructor: Prof. V. Cerasi)

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

Mandatory

- Statistical Inference II (Instructor: TBC)

- Statistical Inference III (Instructor: TBC)

- Bayesian Statistics I (Instructor: Prof. B. Nipoti)

- Bayesian Statistics II (Instructor: TBC)

- Data Management (Instructor: TBC)

- Computational Statistics II (b) (Instructor: Prof. T. Rigon)

Optional

- Computational Statistics I (a) (Instuctor: TBC)

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

- Innovation and Technology Management I (Instructor: Prof. S. Torrisi)

- Innovation and Technology Management II (Instructor: Prof. L. D’Agostino)

- Innovation and Technology Management III (Instructor: Prof. F. Di Pietro)

- Innovation and Technology Management IV (Instructor: Prof. M. Guerzoni)

- Machine Learning (Instructor: TBC)

- Natural Language Understanding (Instructor: TBC)

- Social Media Analytics (Instructor: Prof. R. Boselli)

 

 

IV Term

The IV term teaching activities start on 4 September 2023 and end on 27 October 2023. The IV term exam session starts on 1 November 2023 and ends on 10 November 2023. 

Note: the timetable of the IV term courses is currently under construction and will be available here.

 

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

- Statistical Learning I (Instructor: TBC)

- Statistical Learning II (Instructor: TBC)

- Statistical Learning III (Instructor: TBC)

- Statistical Modelling I (Instructor: Prof. F. Castelletti)

- Statistical Modelling II (Instructor: Prof. F. Greselin)

- Statistical Modelling III (Instructor: TBC)

- Statistical Modelling IV (Instructor: TBC)

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

- Statistical Modelling I (Instructor: Prof. F. Castelletti)

- eXplainable AI for Business Value (Instructor: Prof. F. Mercorio)

- Deep Learning and Computer Vision for Business (Instructor: TBC)