Course

Year

Hours

Faculty

Description

Advanced Mathematical Methods for Economic Decisions

1

18

Mastrogiacomo, Tarsia

This course provides an introduction to the mathematical frameworks and tools such as linear algebra, dynamical systems, and deterministic control processes, which are essential to understand many economic and financial phenomena. It combines theoretical knowledge with applications, providing a solid mathematical background relevant to various contexts.

Learning Outcomes:

Upon completion of this course, students will be able to:

- Gain foundational knowledge of linear algebra and dynamical systems theory.

- Explore the basics of deterministic control processes.

- Develop skills to model and solve problems in economics and finance.

Course Delivery:

The course is delivered through traditional lectures, which support direct interaction and active engagement between students and instructors. This format is ideal for fostering a comprehensive understanding of complex mathematical concepts applied in economic and financial contexts.

Final Assessment:

For the final assessment, students are required to develop a project that applies the learned material to solve a practical problem in economics or finance. The project culminates in a final presentation and a written report, demonstrating the practical application of the skills acquired throughout the course.

Bibliography:

1.Barro, Robert J., and Xavier Sala-i-Martin. Economic Growth. 2nd ed. Cambridge, MA: MIT Press, 2004.

2. Mikosch, T. (1998). Elementary stochastic calculus with finance in view. World scientific.

3. Simon, C. P., & Blume, L. (1994). Mathematics for economists (Vol. 7). New York: Norton.

Optimization with applications to finance and economics

1

16

Hitaj

Objective of the course:

Students will be able to use the main mathematical instruments to study and solve optimization problems in economics and finance.

Optimization theory:

1- Functions of several variables

2- Unconstrained optimization

3- Constrained optimization with equality constraint

4- Constrained optimization with inequality constraint

5- MatLab: optimization toolbox

Applications to finance and economics

1- Maximization of the Utility function

2- Portfolio allocation: maximizing the utility function of an investor and building the efficient

frontier with only risky asset.

3- Analyzing the impact of introducing lower and upper bounds on the decision variables in

building the efficient frontier.

4- Analyzing how the efficient frontier changes when introducing a risk-free asset.

Model parameter estimation

1- Different estimation methods of the input parameters in a portfolio selection problem.

2- Can we use market indices as market portfolios? Analyzing some results

3- Black-Litterman model (introducing active views)

Stock Market index

1- Building market indexes.

2- Portfolio selection using a CARA utility function and second order Taylor approximation.

3- Buy-and Hold rolling window strategy

4- Efficient frontier under uncertainty

5- The impact of introducing higher moments on portfolio selection

Back-testing procedure

1- Back-testing procedure in case of portfolio selection.

2- In-sample and out-of-sample analysis

3- Sensitivity analysis on the impact of the risk aversion parameter on portfolio diversification.

4- Drawback of the rolling-window strategy

Presentation of the case studies by the students

Bibliography:

1) C. P. SIMON - L. E. BLUME Mathematics for Economists W W Norton & Co Inc, Londra, 1994 » Pagine/Capitoli: Chapters 14,16,17,18,22

2) Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

3) Elton, E., and M. Gruber. “Estimating the Dependence Structure of Share Prices - Implications for

Portfolio Selection.”

Applied econometrics: causality and policy evaluation

1

22

Vezzulli, Porro, Castelnovo, Sonedda

Topics Covered:

1. Causal Inference: Definition, estimation, validity

-The Neyman–Rubin causal model: definition, assumptions and relations with other approaches;

-Regression, IV and Causality: identification and estimation problems;

-Brief introduction to R and STATA.

2. Estimating Causal Effects Using Experimental Data

-Randomized Controlled Experiments and Conditional Randomization;

-Heterogeneity in Treatment Effects and Imperfect Compliance;

-Relationship between ATE, ATT and LATE;

-Spill-overs/Externalities, External Validity and other problems with Randomized Experiments.

-Practical session: examples/take home with R and/or STATA.

3. Quasi-Natural Experiments and Observational Studies

-Quasi- or Natural Experiments;

-IV Approaches and Problems with Weak Instruments;

-Regression Discontinuity Designs: Sharp and Fuzzy RDDs.

-Practical session: examples/take home with R and/or STATA.

4. Selection on observables and matching

-Model dependence and matching;

-Assumptions: SUTVA, Conditional independence, Common support;

-Exact and approximate matching:

a) Propensity Score Matching (PSM);

b) Coarsened Exact Matching (CEM);

-Practical session: examples/take home with R.

5. Difference-in-Differences estimator and other methods

-Difference-in-Differences estimator: assumptions and validity;

-Multiple groups and multiple periods;

-Practical session: examples/take home with R and/or STATA.

Introduction to Macro Agent Based models

1

12

Caverzasi

Topics covered:

1.Limits of standard Macro Models

•Delli Gatti, Domenico & Desiderio, Saul & Gaffeo, Edoardo & Cirillo, Pasquale & Gallegati, Mauro. (2011). Macroeconomics from the Bottom-up. 10.1007/978-88-470-1971-3. (Ch.1)

2.Complexity in Economics and ACE

•Tesfatsion, Leigh, (2006), Agent-Based Computational Economics: A Constructive Approach to Economic Theory, Staff General Research Papers Archive, Iowa State University, Department of Economics.

3.Macro Agent-Based Model

•Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, February. (Chs.1 and 2)

4.AB macro models in R a trivial example

•Caiani, Alessandro & Russo, Alberto & Palestrini, Antonio & Gallegati, Mauro. (2016). Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling. 10.1007/978-3-319-44058-3. (ch.2)

5.An intro to social accounting and the flow of funds

•Eugenio Caverzasi & Antoine Godin, 2015."Post-Keynesian stock-flow-consistent modelling: a survey," Cambridge Journal of Economics, Oxford University Press, vol. 39(1), pages 157-187.

•Wynne Godley & Marc Lavoie, 2007."Monetary Economics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-62654-6. (Ch.2)

6.Intro to Stock-Flow consistent modelling

•Michalis Nikiforos & Gennaro Zezza, 2017."Stock Flow Consistent Macroeconomic Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1204-1239, December.

7.SFC macro models in R a trivial example

•Wynne Godley & Marc Lavoie, 2007."Monetary Economics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-62654-6. (Ch.3)

Decision making in historical-economic perspective

1

12

Brambilla

History–and economic and business history are no exception–can be considered as the result of countless decisions taken by as many different actors, often intertwined with one another. The course aims at showing that all those decisions are taken in a context, a context characterised by uncertainty and by limits to the choices actors can make. And that, in this sense, path dependence and uncertainty may affect the main determinants of economic growth–innovation & technological development, entrepreneurship, and institutional change. The course analyses such issues by presenting some relevant cases in modern economic and business history.

Format

The course lasts 12 hours, divided into two parts:

1.Lectures, 8 hours

2.Students’ presentations, 4 hours

The first part consists of 4 classes of two hours each, during which the instructor introduces and gives the framework of the topic, focussing on some specific issues and examples. Students are expected to prepare the suggested readings and to lively participate to in class discussion

1.General introduction and path dependence

2.Path dependence affecting technological change and innovation

3.Institutional innovation choices and path dependence

4.State owned enterprises, a case study; by a visiting professor

The second part consists of one meeting during which students present a case study and discuss it with the class

Applied econometrics: Health Economics

1

18

Robone, Orso, Riganti

The course provides students with an understanding of the most relevant micro-econometric techniques available to applied researchers, with a particular focus on the use of individual level data in health economics. The purpose of the course is to help students in selecting techniques suited both to their data and to their economic model and illustrate the skills required to put these techniques into practice.

The emphasis of the course is on applied work and on the illustration of the use of relevant computer software (STATA) applied to large-scale survey datasets. The course is computer based and is built around empirical case studies rather than general theory and the emphasis is on learning by example. Relevant methods are presented alongside the Stata code and empirical results are discusses in class. Practical applications of the methods are illustrated using data on health from, among others, the British Household Panel Survey (BHPS) and the WHO World Health Survey (WHS).

By the end of the course, participants should be able to:

• formulate empirical problems involving micro-data

• select appropriate econometric methods

• understand methods of estimation and be able to implement them, using appropriate software • construct usable datasets and know the limitations of the data

• interpret the results of the analysis

Course content

The course will cover the following topics:

• survey design;

• binary choice models and models for categorical data;

•anchoring vignettes and hierarchical models;

•count data regression;

•models and methods for panel data

•field experiments in health (with and without compatible incentives)

The economics of altruistic decisions and charitable giving

1

10

Galmarini

The lectures are divided into three parts.

Part I gives a brief introduction to the theory of charitable giving, focusing on the motivations and the incentives of donors.

Part II reviews the literature on laboratory experiments aimed at testing the predictions of the theoretical models illustrated in Part I. Part III presents some topics on the ongoing empirical research, based on laboratory and field experiments, examining how donors respond to various fundraising strategies adopted by charitable organizations. A throughout coverage of the issues can be found in the surveys by Andreoni (2006), Andreoni and Payne (2013), Vesterlund (2015).

Part II and III of the lectures illustrate and discuss a selection of research papers. For the final exam, each student is asked to make an oral presentation of one or two research papers at her/his choice. on charitable giving.

Advanced Econometrics for Decision Making

1

12

Seri

The aim of this course is to equip students with a comprehensive understanding of maximum likelihood estimation, linear regression, discrete choice models, and testing methodologies. The course intends to develop students’ ability to build, test, and interpret statistical models.

Brief course syllabus:

Mathematical and programming preliminaries

Maximum likelihood estimation:

-Parameters, estimators and estimates

-Criteria for estimators

-Maximum likelihood estimation: matrices, likelihood function, maximum likelihood estimators, convergences, consistency and asymptotic normality, efficiency

-Maximum likelihood inference: significance and hypothesis testing, confidence intervals

Linear regression in a nutshell

-A gentle and formal introduction

-The MLE: hypothesis, the asymptotic properties of the MLE, finite sample properties of the MLE

-Model building and testing: the general philosophy of model building, significance tests, diagnostic tests

-Three examples

Discrete choice models

-Non regression models

-Reduced-form models: general topics, the probit model, the logit model

-Structural models: biological interpretation, psychological interpretation, economic interpretation – stochastic utility and latent regression

-An example

Testing: problems and pitfalls

-Single test: preliminaries, significance testing, hypothesis testing, NHST, confidence intervals, information criteria

-Multiple tests: multiple tests at once, sequential tests, meta-analysis

References

Readings and further references will be provided together with the course materials.

Transport decision making and sustainability

1

12

Maggi

Aim and contents of the course

Transport has a key role for the socio-economic wellness of any society, but it is also one of the most important sources of pollution, other negative externalities, and greenhouse gas emissions, affecting the climate changes.

The course provides students with an understanding of the most relevant and recent topics reflecting latest research debate about transport sustainability and related models for the analysis, by applying an interdisciplinary approach, in the framework of Sustainable Development Approach (United Nations’ Agenda 2030 SDGs). The focus is on the impacts of the individual and organization decisions about mobility, on the transport sector transition and on public policies for sustainability.

Topics and lecturers:

-Transport externalities and sustainable transport policies (3h)- Elena Maggi, University of Insubria

-Sustainable Mobility Contribution of New Business Models and Technologies in the Transport Sector (3h)- Graham Parkhurst, University of West England Bristol-UK

-Active and sustainable mobility in later life (2h) – Daniele Crotti, University of Insubria

-Travel trends and behaviour for a sustainable future (6h), Kiron Chatterjee, University of West England Bristol-UK

Networks: Theory and Applications

1

12

Vanni

The course aims to introduce some notions of modern complex networks as an analysis

tool and interpretation of many real-world phenomena.

SYLLABUS:

• What is a network:

- Interconnected systems and complex phenomena

- Examples: Social and Economic Networks

•Basic Definitions:

- Multigraphs and digraphs

- Connectivity & Walkability on a graph

•Graph Structures: trees and bipartite graphs

•Mathematical representations of network graphs

•Measures and metrics on networks:¿Distribution of metrics and measures in large networks

- Correlations in Networks

•Models of Network Formation:

- Random-Networks & Preferential attachment

- Hidden-variable model & Configuration model

- Temporal Networks and stochastic evolution

- Shocks and Cascades

•Characterizations of Complex Networks and emergent properties

•Empirical evidences and applied models:

- Human Mobility and Transport Networks

- World Trade Flow

- Inter-bank and Financial Networks

- Environmental Input-Output Production Models

References:

•Estrada E., Knight PA. A first course in network theory. Oxford University Press,USA; 2015.

•Barabasi A., Network Science, Cambridge University Press, 2016,

•Latora V., Nicosia V, Russo G. Complex networks: principles, methods and

applications. Cambridge University Press; 2017,

•Newman M,Networks, Oxford University Press, 2018

•Dorogovtsev SN, Mendes JF.The Nature of Complex Networks. Oxford University

Press; 2022

Assessment:

A final brief report an selected topics will be required to be written at the end of the course

Other courses

STATA Laboratory for financial and economic analyses 

1

10

Tanda

STATA Laboratory for financial and economic analyses 

The seminars aim at providing the students with the knowledge of the basic tools in Stata for data description and modelling for financial and economic analyses. In particular, the first two seminars (1 and 2) are dedicated to the basic commands and features of Stata and linear regression, while the seminars 3 and 4 are devoted to panel regression and hints of alternative estimation techniques for different types of data, such as count data.

Topics covered

• Introduction to Stata o Interface

o commands

o files (dta, do and log files)

o variable creation and data upload

o macro

o descriptive statistics, estimation and postestimation commands o graphs

• Linear regression and IV regression

o commands of linear regression, estimation and postestimation

results interpretation

o IV regression in Stata

o endogenous treatment effects

• Panel regression

o panel settings

o panel regression

o random effects and fixed effects, Hausman test o dynamic panel data (hints)

• Count data

o Poisson and Negbin

o graphs

o interpretation of results

Introduction to MATLAB

1

8

Moretto

Introduction to MATLAB

The aim of this brief course is to provide a basic knowledge of one of the most known and used numeric computing environment: Matlab. Università dell’Insubria provides all its students and faculty members a Matlab campus license.

Topics covered

•Numbers, vectors, matrices, and variables management

•A review of basic topics in linear algebra

•Function in Matlab

•Matlab’s M-files

•(Very) simple algorithms in Matlab

•Unconstrained and constrained optimization in Matlab

•(A very brief) introduction to simulation techniques

Other lectures and seminars held by visiting professors external to the faculty.

1, 2, 3

16

Course

Year

Hours

Faculty

Description

“Innovation Camp” for Insubria PhD students - A deep dive into innovation and execution

1 or 2

36

Fasano, Pisoni, Vezzulli, Farao, Capelli, Bellucci

Objective: According to EU Council, entrepreneurship is one of the eight Key Competences for Lifelong learning. Innovation Camp for PhD Students is a course dedicated to the dissemination of entrepreneurship and innovation concepts among PhD students and to the development of the related hard and soft skills. It begins from the milestones of the lean startup approach up to the development of an innovative idea. At the end of the course an open badge will be issued to all participants who attended both the open day and at least 75% of the remaining proposed activities.

COURSE TOPICS

Open day: Research potential, entrepreneurship, and technology transfer (4 h)

Introduction to startup world (4 h)

Legal aspects (4 h)

The Lean Startup with hands-on (4 h)

Funding and supporting the idea (4 h)

Communication of the idea (4 h)

Team working & mentoring activities (4 h)

Pitch refinement session (4 h)

Final presentation of business ideas (4 h)

Safety in the laboratory

1

14

Lucarelli, Fanetti, TBD

Legal aspects. Working with videoterminals. Working with chemicals. Working with lasers and radioactive sources. Biohazard.

Artificial intelligence

Any

8

Ref. Fasano

Foundations of AI. The AI act. Applications (Biology, Surgery, Medicine, Economics, Humanities, Astrophysics, Materials science)

Research integrity

Any

12

Ref. Cosentino

The course aims to promote knowledge of the principles and standards defined in the European Code of Conduct for Research Integrity (https://allea.org/wp-content/uploads/2023/06/European-Code-of-Conduct-Revised-Edition-2023.pdf), providing essential tools for their application in various contexts where scientific research is conducted. It takes into account the roles of the different figures involved in various capacities, their tasks and responsibilities, as well as the pressures each may face from time to time.

The code applies to all scientific and humanities disciplines and promotes the importance of honesty and collaboration in the research process. The research community has the responsibility to formulate principles, ensure the quality and integrity of research, and actively respond to situations where forms of scientific misconduct occur. The code aims to strengthen this responsibility and provide tools to prevent and – if necessary – recognize and manage violations of research integrity.

Academic writing and publishing

Any

8

Ref. Vezzulli

By the end of the course, students should be able to: craft texts in different genres (e.g., summary, problem statement, annotations, etc.); produce an original academic research paper in your field of studies; practice analysis in written form through synthesis of academic papers; provide constructive feedback to peers on their written work, and address issues identified by the instructor and peers when revising one’s own written work.

Personal branding

Any

12

TLC

At the end of the course, the participant will be able to effectively manage their presence on social media by creating high-quality content and will know how to communicate in an official capacity to best promote themselves on their personal and professional channels.

Public speaking

Any

8

TLC

The course introduces important elements of successful presentations including effective listening, presentation organization, and logical structure; informative and persuasive speech; use of visual aids, research, and evidence; ethical considerations; and techniques for building confidence in public speaking.

Objectives: to increase confidence and poise when speaking to audiences or groups; to expand student’s abilities with computer mediated communication in order to better prepare them for future presentations online; to enrich students’ ability to master all components that make a speech successful: understanding timing, figuring out how much practice is needed, ensuring deliverables are clear, and being able to meet deadlines.

Project management

Any

8

TLC

How to start, define and organize a project; how to develop a project plan, including scoping, sequencing tasks, and determining the critical path; how to assess, prioritize and manage project risk; how to execute projects and use the earned value approach to monitor and control progress