Training and Research
PhD Programme Courses/classes
This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!
Introduction to Economics
Credits: 5
Language: English
Teacher: Roberto Ricciuti
Mathematics
Credits: 3.8
Language: English
Teacher: Andrea Mazzon
Probability
Credits: 7.5
Language: English
Teacher: Marco Minozzo
Mathematical Statistics
Credits: 5
Language: English
Teacher: Lorenzo Frattarolo, Claudia Di Caterina
Continuous Time Econometrics
Credits: 5
Language: English
Teacher: Chiara Amorino, Amorino Chiara, Cecilia Mancini
Macroeconomics I
Credits: 7.5
Language: English
Teacher: Khalid W A Shomali, Alessia Campolmi
Microeconomics 1
Credits: 7.5
Language: English
Teacher: Claudio Zoli, Martina Menon, Maurizio Malpede
Field Experiments
Credits: 1
Language: Italian
Teacher: Pol Campos
Game Theory
Credits: 5
Language: English
Teacher: Francesco De Sinopoli
Elements of Financial Risk Management
Credits: 2.5
Language: English
Teacher: Prof. Kim Christensen
Stochastic Optimization and Control
Credits: 5
Language: English
Teacher: Athena Picarelli
Financial Time Series
Credits: 5
Language: English
Teacher: Giuseppe Buccheri
Job Market Orientation
Credits: 1
Language: English
Teacher: Simone Quercia
Advice to Young Researchers
Credits: 4
Language: English
Teacher: Marco Piovesan
Finanza Matematica
Credits: 5
Language: English
Teacher: Guido Gazzani, Alessandro Gnoatto
Behavioral and Experimental Economics
Credits: 4
Language: English
Teacher: Simone Quercia, Maria Vittoria Levati, Marco Piovesan
Stochastic Processes in Finance
Credits: 5
Language: English
Teacher: Sara Svaluto-Ferro
Health Economics
Credits: 4
Language: English
Teacher: Paolo Pertile
Development economics
Credits: 4
Language: English
Teacher: Federico Perali
Political Economy
Credits: 4
Language: English
Teacher: Emanuele Bracco, Roberto Ricciuti
Inequality
Credits: 4
Language: English
Teacher: Francesco Andreoli, Claudio Zoli
Quantitative research methods
Credits: 6.8
Language: English
Teacher: Luca Grassetti, Francesca Visintin, Laura Pagani
Mathematics (2024/2025)
Teacher
Referent
Credits
3.75
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
The course aims to provide students with the tools needed to quantitatively address the main problems that arise in the economic and financial fields. The basic notions of Linear Algebra and Calculus for functions of one variable are essential prerequisites. After an introduction to some more advanced notions of Linear Algebra and Calculus for functions of several variables, the unconstrained and constrained optimization problems and their applicability in the economic-financial field are presented. The resolution of optimization problems will be addressed with the classic results deriving from the conditions of optimality of the first and second order and from the properties of the Lagrangian function.
Prerequisites and basic notions
You are expected to be familiar with standard calculus in one variable
Program
Linear algebra: matrix algebra, determinants, rank, quadratic forms, sign of a quadratic form and
definite matrices.
Calculus: functions of several variables, level sets, differential calculus for functions of several
variables, convex functions.
Unconstrained optimization: first order optimality conditions, second order optimality conditions.
Constrained Optimization: the Weierstrass Theorem. Constrained optimization with equality
constraints, Lagrange theorem. Lagrangian function and optimality conditions. Constrained
optimization with inequality constraints, Kuhn-Tucker theorem. Convex problems.
Didactic methods
Frontal teaching.
Learning assessment procedures
Written exam.
Assessment
The ability to solve the exercises, the knowledge of the basic definitions and the important theorems, the critical attitude will be considered fundamental
Criteria for the composition of the final grade
Global evaluation on the knowledge of the different topics presented during the course
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Tuesday 01 October 2024 11:00 - 13:00 Duration: 2:00 AM |
To be defined | Andrea Mazzon | R^n as a linear space, linear combinations of vectors, linear independence, dimension of a space, basis of a space. Identity, diagonal, symmetric matrices, transpose of a matrix, matrices product. Linear transformations and matrices. |
|
Thursday 03 October 2024 10:00 - 13:00 Duration: 3:00 AM |
To be defined | Andrea Mazzon | Definition of rank of a linear transformation and of a matrix. Characterization, properties and computation of the rank and of the determinant of a matrix, with examples. Invertibility of a matrix and connection with the determinant. Linear systems of equations. General results of the existence/unicity of solutions. Eigenvalues and eigenvectors of a matrix. Sign of a matrix. |
|
Tuesday 08 October 2024 11:00 - 13:00 Duration: 2:00 AM |
To be defined | Andrea Mazzon | (Leading) principal submatrices, (leading) principal minors. Connection with the sign of a matrix, with examples. Quadratic forms: definition, examples, associated matrices, sign. |
|
Thursday 10 October 2024 10:00 - 13:00 Duration: 3:00 AM |
To be defined | Andrea Mazzon | Determining the sign of a quadratic form looking at the sign of the associated matrix, with examples. Unconstrained optimization: definition of global and local (strict) minimum and maximum points. Examples for quadratic forms. Partial derivatives, functions of class CO, Cl and C2. Gradient, Hessian matrix. First order necessary conditions, stationary points. |
|
Tuesday 15 October 2024 11:00 - 13:00 Duration: 2:00 AM |
To be defined | Andrea Mazzon | Second order sufficient optimality conditions: statement, examples in the space of real numbers, examples in R^2. |
|
Thursday 17 October 2024 10:00 - 13:00 Duration: 3:00 AM |
To be defined | Andrea Mazzon | Second order necessary optimality conditions. Examples and exercises with local study for solving ambiguous cases. Definition and characterisation of convex and concave function. Study of maximum and minimum points of convex and concave functions. |
