ECONOMICS AND DATA SCIENCE

The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes.

This course will help students understand how to deal with large amounts of data. The idea is that students with no technical background in data science or machine learning can start understanding the mechanisms behind this world. We will begin with key ideas on data analysis and describe modern techniques used to make predictions, find patterns, and generate new data. A special focus will be given to regularization, a method used to improve the performance of models and avoid overfitting in machine and deep learning. Students will work in interactive coding environment and explore a new way of programming and learning to program, by exploiting the capabilities of transforming natural to formal language of LLMs. This ``vibe coding'' will make students able of programming with production ability, even without a standard basis of programming skills. Therefore, students will be asked to create your own data analysis project, based on real-world data, and present it at the exam.

The exam consists of two parts:
A “vibe coding” project for data analysis
An oral presentation of the project

• Final project and oral presentation (50%)
  50% of the final evaluation will be based on an individual or group "AI-powered" project and its presentation, for which each candidate will have a maximum of 20 minutes. The evaluation will consider correctness, creativity, analytical rigor, clarity, and critical thinking. In the case of group projects, each candidate must present a self-consistent part of the project.
• Oral discussion (50%)
  During each candidate oral presentation, theoretical concepts will be discussed, and a more in-depth analysis of the final project will be conducted. Evaluation criteria: linguistic clarity, theoretical mastery and connection between theory and practice.

• T. Hastie, R. Tibshirani, J. Friedman, “The Elements of Statistical Learning”, Springer - C. Bishop, “Pattern Recognition and Machine Learning”

The course of Economic Analysis is divided into two distinct teaching modules. The first is entitled Microeconomics and it aims at providing the theoretical tools that allow students to understand the logic underlying the microeconomic choices made by different economic agents, even in contexts of strategic interaction. The second module is instead entitled Macroeconomics and its objective is to illustrate the main economic models that describe the behaviour of firms regarding the level of productive investments undertaken, the occurrence of financial crises as well as the explanation of the monetary policies implemented by the central bank.

The course of Economic Analysis provides some advanced topics of microeconomics and macroeconomics.

The aim of this course is to provide students with the necessary tools to analyse and solve economic questions empirically using microeconomic data. Students will learn to handle questions of causality and how to interpret empirical results of economic policy evaluation. The course is based on the statistical software for data science STATA. During the course we will conduct several empirical applications in the field of labor economics and economic policy evaluation such as the gender wage gap or the evaluation of specific job market programs.

This course aims to provide an introduction to policy evaluation methods. Specifically, the main econometric techniques used for the evaluation of public policies will be discussed during the lectures, introducing students to the counterfactual causality model and providing them with a unified framework for addressing questions about the estimation of causal effects. The course focuses on the econometric intuition underlying the methodologies rather than their formal demonstrations. The course is based on a textbook and lectures, as well as practical exercises using the statistical software Stata.

Final written exam. Parts of the exam may require interpreting Stata output and commenting on tables showing empirical results. In case of special needs, students with disabilities and/or learning disorders are kindly requested to contact the Student Disability Service and the instructors at the beginning of the semester.

The final grade will be based on a written exam (1.5 hours) consisting of three sections. One of these requires students to interpret Stata output and comment on tables displaying empirical model estimates. Each section is worth 10 points. The final grade is the sum of the points earned across the three sections (maximum: 30 points).
Students attending the course will have the opportunity to work on a group project. Each student will be assigned to a group at the beginning of the semester (details will be published and communicated via AulaWeb). The group work may involve the analysis of a scientific article, the preparation of Stata code for data analysis, the writing of a short report, and an in-class presentation. Students will have time to work on the project—also during class sessions—throughout the semester. Presentations will take place during the last week of the course, and the report along with the code must be submitted before the first available exam session. The project can contribute up to 4 extra points to the final exam grade.
Active participation in class exercises may also be rewarded.

Lecture slides, scientific articles, and additional materials (e.g., scripts and datasets) will be made available on AulaWeb. The slides summarize the key points covered in the course. To gain a deeper understanding of each topic, students are encouraged to study Causal Inference: The Mixtape by Prof. Scott Cunningham, which will serve as the main textbook for the course. Angrist’s video course (Mastering Econometrics, n.d.) offers a useful non-technical overview of the methods. Additional references—such as selected scientific articles—will be provided during the course.
References
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. https://doi.org/10.2307/j.ctv1c29t27
Angrist, J. D. (n.d.). Mastering Econometrics, Marginal Revolution University. https://mru.org/mastering-econometrics (accessed June 9, 2022).
Angrist, J. D., & Pischke, J.-S. (2015). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.
Stock, J. H., & Watson, M. W. (2019). Introduction to Econometrics (4th ed.). Pearson.

The objective of the course are: (i) to make students understand the basic assumptions underlying the economic behaviour under uncertainty; (ii) to equip them with the technical tools adopted in neoclassical financial economics to model the price of securities; (iii) to make students aware of the shortcomings of such a neoclassical approach, putting particular emphasis on some empirical/theoretical puzzles such as the equity premium puzzle and the risk free interest rate puzzle.

The course aims to present the main open questions debated in financil economics, trying to figure out why solutions offered by scholars and specialists are often divergent and not conclusive.
In order to understand these topics it is important to master some basic techniques, that will be studied in the first part of the course

For students who attend the course:
40% of the final mark will depend on a written exam (that can be also done at the end of May, before the official beginning of the exam period) on the topics covered in the class (SEE SYLLABUS below)
30% of the final mark on a topic presented in front of the class in May and chosen among a list that I will provide and present in March.
15% of the final mark on a simple homework: you have 1 million euros and you have to choose how to invest it. Feel free to allocate the money as you wish. In the first week of March you send me by mail a brief report in which you present your portfolio decisions. Mid May, you will send me another document (more detailed this time) in which you motivate why and how you gained or lost money.
15% on the final mark on a homework based on a mean variance exercise using Python.
For students who do not attend the course:
90% of the final mark will depend on a written exam (that can be also done at the end of May, before the official beginning of the exam period) on the topics covered in the class (SEE SYLLABUS below)
10% of the final mark on a simple homework: you have 1 million euros and you have to choose how to invest it. Feel free to allocate the money as you wish. In the first week of March you send me by mail a brief report in which you present your portfolio decisions. Mid May, you will send me another document (more detailed this time) in which you motivate why and how you gained or lost money.

Each student will be able to understand the different theories advanced to understand the financial markets.. In addition, the second part of the course will be useful to evaluate the ability to understand complex issues and explain them to other people.

R. Shiller, Irrational Exuberance, Princeton University Press (third edition)
P. L. Bernestein, Capital ideas, Wiley.
L. Eeckhoudt, C. Gollier, H. Schlesinger, Economic And Financial Decisions Under Risk, Princeton University Press

The main objective of the course is to provide knowledge of the main methods for quantitative analysis and risk measurement in the context of financial markets

The course develops the analytical and quantitative knowledge for risk measurement with a particular focus on financial and banking sector

Learning test about the fundamental methodologies, followed by an oral interview on one of the topics of the course.

Assessment of the learning test and assessment of the understanding of specific topics during an interview.

Slides, multimedia supports and specific books are provided directly to students. In addition to the educational material available on the TEAMS channel (/ aulaweb) of the course, other bibliographic information will be provided upon request.

The course aims to provide students with the adequate tools to understand and critically analyze from the legal point of view the challenges that technological evolution entails for the financial sector, with the objective of acquiring useful skills to apply in the future working environment. Students, after an overview of the main notions and provisions in banking regulation (TUB/CRD-CRR/PSD-PAD/BRRD, etc.) and investment services and financial markets law (Italian financial markets code and relative Consob Regulations; main European Directives and Regulations in the financial law field), will apply such knowledge to the context of the main Fintech/Digital finance categories, delving into the most significant aspects. For each of the main applications, in fact, the students will analyze the most relevant risks and benefits, legal issues raised, regulatory responses and doctrines, with special reference to the Italian and European regulatory framework, in addition to the main international principles and guidelines, with comparative law insights. Lectures providing explanations and commentary on the laws will be accompanied by the analysis of practical cases, judicial decisions and collective discussions and practical exercises, in order to allow students, not only to understand the topics discussed from a legal point of view but also to acquire the tools to navigate with confidence and at a professional level the new digital economy, applying legal rules and reasoning to practical cases and specific issues. Consequently, the course has a multi-disciplinary approach, addressing aspects of law and economics, ethics of law/finance and sociology. Experts from the sector will be invited to share their precious experience and knowledge.

The course aims to provide students with the adequate tools to understand and critically analyze from the legal point of view the challenges that technological evolution entails for the financial sector, with the objective of acquiring useful skills in the future working environment. It will focus on the Italian and European law applicable to Fintech/Digital finance and, therefore, to financial services provided through the use of the most important, recent and continously evolving technologies, underlying the main legal issues and possible solutions. The entire course will be offered in English.

Oral exam but active participation in class and presentations and comments at seminars will be taken into account for the final grade of students regularly attending classes.

During the exam, students will be assessed based on their knowledge about the topics covered in class, as well as their understanding of the digital finance market, forces and rationes behind regulatory frameworks adopted in this area as well as for the acquisition of an adequate level of specialistic language.

Slides and recommended material will be available through Aulaweb 2026/2027.
For the general part on financial regulation, students could refer, during the academic year 2025/2026, on the following handbooks:
o De Poli Matteo, Fundamentals of European Banking Law, Kluwer, 2020 + Rüdiger Veil, European Capital Markets Law, Bloomsbury 2020 (or more recent edition)
o Matthias Haentjens & Pierre De Gioia Carabellese, European Banking and Financial Law, Routledge, 2020 (or more recent edition)
For 2026, it will be also available:

Financial dynamics complexities, and their geopolitical consequences will be reconstructed on a global scale in a synthetic way. The course will analyze the metamorphoses of the economic theory deriving from the transformation of social, economic, and demographic conditions of some of the most important countries in the world. The course aims to provide students with some basic tools to understand contemporary phenomena, such as financial capitalism, its institutional and economic implications, neo-mercantilism, and the reasons behind the increase in crises. Students will learn to use the World Bank dataset.

The course focuses on some of the biggest global financial and geopolitical issues, crises, and some macroeconomic imbalances trying to analyze them in the light of some of the most important economic theories and considering some specific case studies

Attending students can get an intermediate test.
Students will do team working activities.
Non-attending students will take a written test with multiple choices and two open-ended questions

The intermediate test will check and assess students' knowledge of the main topics and technicalities.
Multiple-choice questions will check technicalities and specific contents
Open questions will check students' critical skills and their ability to elaborate logically the topics taught in the course Team working will check student's critical and dialectical skills

J. Ingham, Capitalism, part.1 - Classical theories of capitalism
1)Smith: p. 7-14
2) Schumpeter and Keynes: p. 36-51
Ingham, part. 2 - The instititutions
1) Money: p. 65-91
FRANCESCO PAOLO MONGELLI, "New" views on the Optimum Currency Area theory: What is EMU telling us? - European Central Bank Working Paper Series, 138/2002.
A. ALJADANI, M. FEAD, L. RAIMI, Exploring the potentials of optimum currency area and the political will of the Gulf Cooperation Council, Proceedings of 4th European Business Research Conference 9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
FU-LAI TONY YU, NeoMercantilist Policy and China’s Rise as a Global Power, in Contemporary Chinese Political Economy and Strategic Relations: International Journal, Vol. 3, No. 3, Dec. 2017, pp. 1043-1073
A. SIERON, Endogenous versus exogenous money. Does the debate really matter?, Research in Economics, 2019
Other materials will be uploaded to Aulaweb page

By the end of the course, students will be able to:
Analyze the major economic, political, social, and environmental crises of the current context, examining their scope and interrelationships within the framework of the polycrisis;
Analyze the role of uncertainty and risk in the transformation of consumer behavior, with reference to the building of trust between businesses and consumers;
Design trust-oriented marketing strategies in response to the market’s ethical and environmental expectations;
Define green marketing and sustainability marketing, situating them within the broader debate on corporate sustainability;
Recognize the importance of approaches aimed at creating positive externalities in economic, social, and environmental contexts;
Analyze the external and internal environments to understand the target market according to the principles of analytical marketing;
Segment the market based on sustainability and responsibility criteria, identifying the most relevant target groups and defining a positioning consistent with the values of contemporary consumers according to the principles of strategic marketing;
Describe the behaviors of sustainable consumers, with particular attention to demands for consistency and transparency in corporate operations;
Interpret sustainable consumption trends at the national and international levels, including through data and market research.

Program Overview:
The current consumer landscape: global and local trends, new lifestyles, and consumer cultures;
Contemporary crises: economic, political, climate, health, and social;
The concept of a polycrisis: origins, dynamics, and implications for the market;
Uncertainty, risk, and trust: how expectations and purchasing behaviors are changing;
Definition of trust-oriented marketing;
Definition and scope of green marketing and the broader field of sustainability marketing: origins, evolution, and prospects;
The new consumer: profiles, values, and motivations of the sustainable and responsible consumer;
Definition of the attitude-behavior gap: potential obstacles and solutions;
The importance of aligning corporate values and brand ethos with actual practices;
Analytical marketing: analysis of the external, competitive, and internal environments to assess the market situation in which the company operates;
Strategic marketing: segmentation, targeting, and positioning in the sustainability market;
Emerging trends: from the circular economy to the regenerative economy;
Case studies to help understand the theoretical concepts covered.

The exam consists of a written test designed to assess the student’s ability to discuss the topics covered in the course in a critical, comprehensive, and informed manner, drawing on concrete examples and real-world contexts.

The evaluation takes into account the accuracy and completeness of the answers, the ability to relate the course’s theoretical content to real-world contexts and case studies, the depth of critical analysis, and the quality of presentation and argumentation.

The required reading is:
Belz, F.M.; Peattie, K. (2012). Sustainability Marketing - A Global Perspective.
Digital note: https://www.egeaeditore.it/ita/prodotti/marketing-e-vendite/sustainability-marketing.as
Supplementary teaching materials (slides, scientific articles, industry reports, and case studies) will be provided via the University’s e-learning platform.

This course provides advanced economics concepts to analyze the central issues in health economics and health policy. The course is intended to ensure that students have command of the main tools used in public economic analysis. Students are introduced to economics of information and to “mathematical modes of analysis” and provided with sufficient command of such analysis to enable them to undertake advanced theoretical and empirical work in the areas of health and public economics. In particular students will improve their understanding of and skills in the “wide” field of health economics: health policy, pharmaceutical economics and policy, health care programs, economic evaluation, health care market regulation. As part of the course there is the laboratory of health Economics. The laboratory aims at using a data-driven approach to objectively prioritize policy choices in the field of health economics. By the use of Stata software, the laboratory will show how to exploit large-scale datasets and advanced economic and econometric techniques to identify the most effective and fair policy actions to govern ageing societies (e.g. selection of value for money technologies; new drugs distribution channels; strengthening of territorial medicine; management paths of chronic conditions; visits and exams with extended hours and on weekends; telemedicine).

This course provides advanced economics concepts to the central issues in health economics and health policy.
The course is intended to ensure that students have command of the main tools used in public economic analysis.
In particular students will improve their understanding of and skills in the “wide” field of health economics: health policy, pharmaceutical economics and policy, health care programs, economic evaluation, health care market regulation.

An important element of the course is the two (written) intermediate tests that form the continuous assessment element, one in the middle and one at the end of the course. They will also provide feedback on the development of the student's understanding. Alternatively, students are assessed by a written/oral final examinations. Attendance to the course is not required and there is no difference between attending and nonattending students.

The intermediate test will check and assess students' knowledge of main topics and tecnicalities. Multiple choice questions will check technicalities and specific contents. Open questions will check students' critical skills and their ability to elaborate logically the topics taught in the course. Team working will check student's critical and dialectical skills

Handouts, slides, exercises and any other material to support the lectures will be made available by AulaWeb

Students will receive a graduate-level introduction to the economic theories, models, and empirical evidence of modern macroeconomics, with particular reference to the economics of growth. By the end of the course, students will have acquired the knowledge necessary to summarize, reproduce, and manipulate various macroeconomic models, to derive and analyze the associated policy recommendations, and to engage with existing empirical evidence. With this knowledge, students will be able to use theoretical and applied macroeconomic techniques to understand the proximate causes of growth, the role of physical and human capital accumulation and technological progress, and the influence of public policies and changes in economic fundamentals. Furthermore, students will acquire the basic conceptual and mathematical tools necessary to read and comprehend the contemporary literature in theoretical and applied macroeconomics.

Why are some countries rich and others poor? What types of countries grow more rapidly? Why do certain societies fail to improve their technologies? This course will introduce students to these major questions and to the theoretical, mathematical, and conceptual tools necessary for answering them. The presentation of the theories will be supplemented with examples of relevant empirical works and policy implications.

The exam consists of a single written examination on the full contents covered in the most recent version of the course. The written exam lasts 90 minutes and consists of two questions, each worth 50% of the final mark. Questions may include open-ended theoretical questions, analytical derivations and/or exercises. Students must register for the exam according to the official procedure. Registering for a new exam session implies rejection of any previous mark obtained in the same course.
There are no mid-term exams. The exam dates will be communicated in advance through the official channels. No additional exam dates will be offered outside the scheduled sessions.
In case of suspected irregularities during the exam, students may be required to take an additional oral examination.

The written examination is designed to assess whether students have achieved the expected learning outcomes.
Theoretical questions assess students’ knowledge and understanding of the main macroeconomic models, their assumptions, mechanisms, implications and limitations.
Analytical derivations and exercises assess students’ ability to reproduce and manipulate formal models, derive equilibrium conditions and steady states, perform comparative statics and interpret the economic meaning of mathematical results.
The assessment will take into account the correctness and completeness of the answers, the ability to use the appropriate analytical tools, the clarity of the economic reasoning, the correct use of macroeconomic terminology, and the ability to connect model predictions with empirical evidence and policy implications.
For students taking the exam in the first available exam session, additional points may be awarded for homework submitted during the course, according to the rules and deadlines communicated on AulaWeb. These additional points are valid only for the first available exam session and expire at the end of that session.

There is no single textbook for the course. The main references are:
David Romer, Advanced Macroeconomics, McGraw Hill.
Robert J. Barro and Xavier Sala-i-Martin, Economic Growth, MIT Press.
Daron Acemoglu, Introduction to Modern Economic Growth, Princeton University Press.
Marina Azzimonti, Per Krusell, Alisdair McKay and Toshihiko Mukoyama, Macroeconomics.
Useful but more advanced references include:
Nancy L. Stokey, Robert E. Lucas and Edward C. Prescott, Recursive Methods in Economic Dynamics, Harvard University Press.
Lars Ljungqvist and Thomas J. Sargent, Recursive Macroeconomic Theory, MIT Press.
Additional references may be indicated during the course. Lecture slides, tutorials and, where appropriate, solutions or additional material will be made available on AulaWeb.

Upon completion of the course, students will be able to:
Apply the four elements of the marketing mix (product, price, communication, distribution) in business contexts related to energy and environmental sustainability, highlighting potential deviations from traditional models;
Assess a product’s sustainability throughout its life cycle, including through tools such as LCA (Life Cycle Assessment);
Analyze product strategies and packaging design in relation to the cradle-to-cradle approach—that is, circularity—and the environmental certifications obtained;
Analyze sustainable pricing strategies, willingness to pay for green attributes, and their market acceptance in terms of the gap between claims and actual behavior;
Examine the components of Total Consumer Cost (TCC), highlighting how a product that is more expensive in the short term may prove more economical in the long term due to the energy savings it offers;
Distinguish between authentic environmental communication and greenwashing, understanding the ethical, legal, and reputational implications, particularly in light of current regulations;
Monitor and manage online reputation in the context of environmental transition, using social listening tools such as sentiment analysis, content analysis, and emotion detection;
Design credible sustainable communication plans that are consistent with the company’s identity, its values, and the concrete actions it has implemented;
Evaluate the most appropriate distribution channels for products and services to reduce environmental impact, including digital and local distribution solutions.

Program Overview:
The operational marketing mix from a sustainability perspective: an introductory overview and key features;
The product lever: eco-design, product life cycle, environmental certifications, and international standards demonstrated through product packaging, and the cradle-to-cradle approach;
The price lever: sustainable pricing, perceived value, willingness to pay (the gap between stated and actual behavior), and models based on Total Consumer Cost (TCC);
The communication lever: environmental storytelling, authenticity, transparency, and consistency with corporate objectives, mission, and values;
Negative situations such as greenwashing: definition, types, emblematic cases, and the European regulatory framework;
Online reputation assessment through social listening, sentiment analysis, content analysis, and emotion detection;
The distribution lever: sustainable channels, green logistics, digital distribution, and selection of approaches between long and short channels;
Case studies: analysis of strategies implemented by players operating in relevant sectors and examination of future scenarios such as decarbonization and the new frontiers of environmental marketing;

The exam consists of a written test designed to assess the student’s ability to discuss the topics covered in the course in a critical, comprehensive, and informed manner, drawing on concrete examples and real-world contexts.

The evaluation takes into account the accuracy and completeness of the answers, the ability to relate the course’s theoretical content to real-world contexts and case studies, the depth of critical analysis, and the quality of presentation and argumentation.

The required reading is:
Belz, F.M.; Peattie, K. (2012). Sustainability Marketing - A Global Perspective.
Digital note: https://www.egeaeditore.it/ita/prodotti/marketing-e-vendite/sustainability-marketing.as
Supplementary teaching materials (slides, scientific articles, industry reports, and case studies) will be provided via the University’s e-learning platform.

The goal of the course is to provide the quantitative tools and knowledge to address the other courses of the Master program

The aim of the course is to provide the student with the knowledge of advanced mathematical methods to successfully deal with economic models from a quantitative viewpoint.

The exam will be written and will contain questions on the the theoretical and modeling features treated in the course as well as exercises.

The exam will evaluate the understanding of the contents of the course with the goal of assessing the reached skill of applying the tools and the methods learned in an economic perspective.

• Simon, C., Blume, L.: Mathematics for Economists (1994), Norton and Company
• Peccati, L., D'Amico, M., Cigola, M. : Maths for Social Sciences (2018), Springer
• Knut Sydsaeter, Peter Hammond, Atle Seierstad, Arne Strom, "Further mathematics for Economic Analysis" (2nd edition), Pearson

The course aims to provide students with a basic undergraduate background in Economics with the quantitative tools needed to fully engage with the courses of the Master’s program. Specifically, the course complements the contents of a standard undergraduate Mathematics course by covering topics in linear algebra, functions of several variables, and static optimization (both unconstrained and constrained). Particular attention is devoted to applications and examples based on economic models.

The aim of the course is to provide the student with the knowledge of advanced mathematical methods to successfully deal with economic models from a quantitative viewpoint.

The exam will be written and will contain questions on the the theoretical and modeling features treated in the course as well as exercises.

The exam will evaluate the understanding of the contents of the course with the goal of assessing the reached skill of applying the tools and the methods learned in an economic perspective.

• Simon, C., Blume, L.: Mathematics for Economists (1994), Norton and Company
• Peccati, L., D'Amico, M., Cigola, M. : Maths for Social Sciences (2018), Springer
• Knut Sydsaeter, Peter Hammond, Atle Seierstad, Arne Strom, "Further mathematics for Economic Analysis" (2nd edition), Pearson

The course introduces the main mathematical tools for the analysis of continuous-time dynamic systems and intertemporal optimization problems. The first part reviews integration theory and ordinary differential equations: first-order separable and linear ODEs, equilibria and stability, second-order linear ODEs with constant coefficients, and first-order linear systems. The second part focuses on continuous-time optimal control through dynamic programming. Topics include the formulation of dynamic optimization problems, the Dynamic Programming Principle, the Hamilton-Jacobi-Bellman equation, the verification theorem, and applications to economics.

The aim of the course is to provide the student with the knowledge of advanced mathematical methods to successfully deal with economic models from a quantitative viewpoint.

The exam will be written and will contain questions on the the theoretical and modeling features treated in the course as well as exercises.

The exam will evaluate the understanding of the contents of the course with the goal of assessing the reached skill of applying the tools and the methods learned in an economic perspective

Sydsaeter, K., Hammond, P., Seierstad, A., Strom, A.: Further Mathematics for Economic Analysis (2008), Pearson
Simon, C., Blume, L.: Mathematics for Economists (1994), Norton and Company
Peccati, L., D'Amico, M., Cigola, M. : Maths for Social Sciences (2018), Springer

The aim of the Module is to equip students with the theoretical knowledge that is necessary in order to understand the micro behaviour of economic agents, also in a strategic context. Moreover, students will be taught the analytical instruments that are crucial in order to read and understand the modern literature in both the theoretical and applied microeconomics. With those instruments, students will learn to apply economic reasoning to consumers as well as firms' decisions, the functioning of markets and how markets are influenced by public policies. The competencies that students will get are complementary to data analysis techniques that students will learns in other modules and are crucial for a correct understanding and interpretation of the empirical evidence in various contexts (e.g. industrial economics, health economics, labour economics and public economics)

The course’s main objective is to provide a postgraduate-level introduction to modern microeconomic theory, with a focus on consumer and producer theory, as well to the analysis of market structures.

Written exam. The program is the same for attending and non-attending students. Any changes due to the COVID emergency will be announced on Aulaweb. In case of special requirements, students with disabilities and/or learning disabilities are requested to contact - at the beginning of the semester - the Student Disability Services and the teacher.

Written exam containing open questions and numerical/analytical exercise. A mid-term evaluation will be scheduled.

Munoz Garcia F. (2017) Advanced Microeconomic Theory. An Intuitive Approach with Examples. MIT Press.

The course provides students the basics of Optimization, which are most relevant to the operational planning of enterprises. The course aims to develop optimization models and provide mathematical programming methods, both exact and heuristic, for decision-makers. Students are also provided with the necessary knowledge to understand the structure of an optimization algorithm and to implement it with Python. By the end of the course, students will have the skills necessary to identify the methodological approach needed to address a problem and the ability to implement in Python that method to determine solutions.

Optimization is a discipline included in decision science and management science. The course introduces the fundamentals of the discipline and the role of the Python programming language in the context of optimization.

The exam consists of a written test comprising short-answer questions, multiple-choice questions, and brief exercises. Students who achieve a minimum grade of 18/30 will pass the exam.

Knowledge and skills will be assessed through online tests and exercises during class, evaluating the ability to apply optimization methods to proposed problems, the correctness of solutions implemented in Python, and clarity of exposition. Assessment criteria include: correctness of answers, appropriate use of terminology, analytical and synthesis skills.

The following books, articles and link are suggested.
Hillier, Lieberman, “Introduction to Operations Research”, McGraw Hill, 2016.
Downey, A., et al. "Thinking python. 2.0". Green Tea Press Supplemental Material:, 2012.
https://coin-or.github.io/pulp/index.html

Students will receive a graduate-level introduction to the economic theories, models, and empirical evidence of modern macroeconomics, with particular reference to the economics of growth. By the end of the course, students will have acquired the knowledge necessary to summarize, reproduce, and manipulate various macroeconomic models, to derive and analyze the associated policy recommendations, and to engage with existing empirical evidence. With this knowledge, students will be able to use theoretical and applied macroeconomic techniques to understand the proximate causes of growth, the role of physical and human capital accumulation and technological progress, and the influence of public policies and changes in economic fundamentals. Furthermore, students will acquire the basic conceptual and mathematical tools necessary to read and comprehend the contemporary literature in theoretical and applied macroeconomics.

The course is structured around three fundamental theoretical problems in resource economics: (I) market failures that result from the extraction of exhaustible resources (II) those that affect markets on which these resources are traded and, finally, (III) the footprint determined by the use of these resources. For all these issues, energy is a key example and, as a result, electricity and gas will be used as the leitmotif of the learning path.

The reference texts and materials supporting the lectures will be made available on the Aulaweb at the beginning of the lessons.
All students are invited to periodically consult the page for this course on the AulaWeb e-learning portal (accessible from the University website or at http://www.aulaweb.unige.it/). All further information and materials relating to this teaching are published exclusively on that site.

The main objective of the course is to provide knowledge of the programming basics of the software R in order to properly develop financial models and to perform statistical analysis

The course is aimed at providing the basic skills related to the use of the R software and, more generally, the concepts inherent to statistical programming with particular attention to economic and financial applications.

The exam is oral.
The test consists in coding and commenting an exercise.

The oral exam allows to verify the actual level of learning in accordance with the objectives and the expected results.
Specifically, the student has to display a satisfactory theoretical approach to the discipline as well as programming skills.
The examination assessment parameters include the quality of the developed code, the functionality, the reasoning and the ability to critically analyze the proposed solutions and the results obtained.

Slides, multimedia supports and specific books are provided directly to students. In addition to the educational material available on the TEAMS channel (/ aulaweb) of the course, other bibliographic information will be provided upon request.

The course aims to provide a comprehensive overview of the main statistical learning techniques, both supervised and unsupervised. To ensure a solid understanding of the methodology underlying statistical learning techniques, the course begins with an introduction to statistical inference, covering both parametric approaches (likelihood-based) and non-parametric approaches (simulation and bootstrap methods). In the area of supervised learning, the course covers the main techniques for regression and classification, including both parametric methods (regression and generalized linear models) and non-parametric methods. In the area of unsupervised learning, topics include clustering and dimensionality reduction techniques. The topics covered and the examples presented enable students to have a solid knowledge of statistical methodology, to be autonomous in addressing data analysis and forecasting problems, and to apply the techniques learned in various contexts, particularly in the economic sciences.

The course “Statistical learning” covers the fundamental elements of both supervised and unsupervised statistical learning. A broad overview of learning techniques is provided: regression models, generalized linear models, non-parametric regression and classification algorithms, model selection methods, unsupervised techniques (cluster analysis and principal component analysis).

Attending students:
The exam consists of a miterm on the first part (likelihood and Monte Carlo methods) and two homeworks (supervised and unsupervised learning), and a final discussion.
Non-attending students:
The exam is written and consists of three questions related to the exam program and referred to the textbooks indicated in the bibliography. The syllabus with the correspondence between the topics in the program and the textbooks will be made available on AulaWeb before the course starts.
The complete exam rules will be available on the AulaWeb page before the course starts.

The exercises in R for attending students must prove: a) mastery of the statistical techniques used; b) the adequacy and correctness in the interpretation of the outputs obtained; c) the ability to frame the statistical analysis within the scope of the proposed application; d) where necessary, the ability to properly use statistical methods not presented during the lessons. During the discussion of the reports, students must prove: a) the correct use of the technical language of statistics; b) the knowledge of the methodology underlying the techniques used for data analysis.
The questions in the written exam for non-attending students are chosen in order to cover, as far as possible, all the topics of the exam program. The proposed questions are intended to evaluate the degree of knowledge of the subject, the acquisition of the correct technical language, the critical capacity of the student and the ability to frame the statistical results within the scope of practical data analyses.

Fetsje Bijma, Marianne Jonker, Aad van der Vaart. An Introduction to Mathematical Statistics. Amsterdam University Press, 2016.
James, Witten, Hastie and Tibshirani. An Introduction to Statistical Learning. With Applications in R. Springer, 2023 (available from the Authors’ webpage).
Hastie, Tibshirani, Friedman. The Elements of Statistical Learning. Springer Springer, 2017 (available from the Authors’ webpage).
Additional course materials will be available on AulaWeb.

Il tirocinio curriculare è un periodo di orientamento e formazione svolto presso aziende, enti pubblici o professionisti convenzionati con l’Università di Genova, durante il quale lo studente ha l’opportunità di applicare in un contesto lavorativo le conoscenze e le competenze acquisite durante il percorso di studio, sviluppare capacità operative e acquisire maggiore consapevolezza nelle future scelte professionali.

La presente scheda riguarda i tirocini da 6 cfu riferiti ai corsi di laurea triennali CLEA,CLEAMLT e SEF e ai corsi di Laurea Magistrali AFC, EDS, EMMP, MAN e MEET afferenti al Dipartimento di Economia.

Al termine del tirocinio, il tutor aziendale redige la relazione finale che viene successivamente sottoposta all’approvazione del tutor accademico. In caso di esito positivo, i CFU acquisiti sono registrati nel libretto elettronico dello studente.