Instructional goals
1) To learn some basic methods in Linear Algebra, Linear and Nonlinear Dynamical Systems, Static Optimization, Dynamic Optimization. These are essential tools to understand and develop mathematical models in economics.
2)To be able to understand mathematical models and to develop them in simple cases
Prerequisites
All basic mathematics courses of Laurea Triennale in Economics and similar topics.
In particular:
- Calculus for one variable functions (basic topology, functions and their properties, limits derivatives and their connection with monotonicity and convexity, integrals, graph of functions);
- Searching extremals and zeros for one-variable functions using the appropriate theorems;
- Basic linear algebra concepts (vector spaces and their bases, linear dependence and independence of vectors, matrices, rank, determinant, linear systems, Rouché-Capelli Theorem)
- Basic calculus for several variables: topology in R^n, limits, continuity, differentiability, gradient and its properties (this part will be briefly reviewed in the first lectures)
Intended learning outcomes
1) Knowledge and understanding:
The course will offer the basic theoretical tools of Linear Algebra, Dynamical Systems, Optimization. These are key tools to understand and develop mathematical models in economics.
2) Applying knowledge and understanding:
The students will be taught how to use the above basic tools to develop simple mathematical models of real phenomena such as:
- population and investment dynamics;
- climate change;
- ranking of web pages;
- economic dynamics;
3) Making judgements:
We expect students to be able to - understand the main mathematical features of basic economic models;
- judge the reliability of information on quantitative modeling that they read in the press;
- build simple mathematical models of real phenomena.
4) Communications Skills:
This course will give the students the possibility to acquire and understand major terms and concepts in order to communicate their ideas, proposals, analysis and critical reasoning in the field of mathematical modeling in the most effective and appropriate way.
5) Learning skills:
This course will contribute to empower learners giving them the tools to evaluate the statements on quantitative mathematical modeling (that they can read in the press or in specialized journals) in an independent way.
Course Contents
- Review of calculus of Several Variables
- Implicit Functions and Comparative Statics
- Unconstrained Optimizazion
- Constrained Optimization
- Eigenvalues and eigenvectors,
- Spectral decomposition
- Linear/Nonlinear Difference/Differential Equations and Systems and introduction to Dynamci Optimization.
- Use of the above techniques to build mathematical models of real phenomena.
Reference Books
1)
MATEMATICS FOR ECONOMISTS
Carl Simon e Lawrence Blume
W.W. NORTON & COMPANY.
2)
Notes given by the teacher.
Teaching Methods
Lessons and Exercises sessions.
“Teaching is not transferring knowledge, but creating the conditions for its production or construction”
Assessment Method
100% final written exam
Thesis assignment criteria
Interview
Week 1 Contenuto sessioni on line e on campus
Introduction to the course and to mathematical modeling.
Review of basic linear algebra.
Week 2 Contenuto sessioni on line e on campus
Linear operators and matrices.
Change of basis.
Complex numbers.
Week 3 Contenuto sessioni on line e on campus
Eigenvalues and eigenvectors. Spectral Decomposition of matrices,part 1
Week 4 Contenuto sessioni on line e on campus
Spectral Decomposition of matrices, part 2.
Week 5 Contenuto sessioni on line e on campus
Linear Dynamical Systems Part 1.
Week 6 Contenuto sessioni on line e on campus
Linear Dynamical Systems Part 2.
Week 7 Contenuto sessioni on line e on campus
Review of linear dynamical systems and applications
Week 8 Contenuto sessioni on line e on campus
Nonlinear dynamical systems part 1.
Week 9 Contenuto sessioni on line e on campus
Nonlinear dynamical systems part 2.
Week 10 Contenuto sessioni on line e on campus
Implicit functions. Unconstrained Optimization. First order necessary conditions.
Second order conditions.
Week 11 Contenuto sessioni on line e on campus
Constrained optimization.
First order necessary conditions.
Constraint qualifications.
Concave and quasiconcave functions.
Week 12 Contenuto sessioni on line e on campus
Envelope Theorems.
Application of Constrained Optimization. Dynamic Optimization