Preparatory classes for freshmen students
First-year students will be offered CRASH COURSES IN MATHEMATICS, ECONOMICS AND ECONOMETRICS in order to acquire the basic tools and pre-requisites needed to successfully attend the RESD programme. These courses are fully integrated into the official timetable, although no credits will be awarded.
I. Introduction to Economics(30 hours, first term)
At the end of the course, students will have sufficient knowledge of the basic concepts of microeconomics (market, price, competition, supply, demand) and macroeconomics (GDP, unemployment, inflation) to undertake the master's degree.
II. Introduction to Mathematics (30 hours, first term)
At the end of the course the student knows the basic mathematical concepts and techniques that are of central importance for modern economic analysis. In particular, the student is able to apply fruitfully standard tools and concepts from differential calculus, such as the implicit function theorem and static optimization; handle the matrix notation and apply linear algebra techniques to the representation and the solution of standard problems from economic theory.
III. Introduction to Econometrics (30 hours, second term)
This course introduces students to linear regression models. It reviews the main theoretical properties of least squares estimation, testing of the parameters under the Gauss-Markov assumptions and discusses the violation of classical assumptions; it provides an introduction to no linear least square and maximum likelihood estimation, and discusses some empirical examples in various fields using the econometric software Stata and EViews.
Crash courses are organised at the beginning of classes, in the month of September and February.
They aim to provide first-year students (particularly those without a bachelor degree in economics or business studies) with the basic tools and pre-requisites needed to successfully attend the programme.
Starting date: 16 September 2024
Room:
Starting date: 16 September 2024
Room:
February 2025
Room: