COMPUTATIONAL TOOLS FOR ECONOMICS AND FINANCE

This is a compulsory course providing notions and skills about programming and the use of software packages for numerical problems, data-analysis and visualization. High-level computational competencies are needed to understand and quantitatively analyze economic and managerial problems and issues.

The course present economic problems of practical relevance that require numerical solutions or quantitative treatment. The powerful and widely adopted software R will be needed along the course (download is free at http://cran.r-project.org/ or https://www.rstudio.com/ ).

a) Knowledge and understanding:
- formal definition of the mathematical problems to be used;
- select the appropriate mathematical tools;
- know which R function/package to use to solve a given problem.

b) Applying knowledge and understanding:
- ability to write some (simple working) code to solve a problem and graphically visualize, whenever possible, the situation or the dataset under examination;
- ability to use and provide suitable inputs to R functions to solve a given problem
- ability to deal with syntax and logical errors and to check the overall soundness of the numerical solution.

c) Making judgements:
- ability to understand (some) relevant issues of an economic problem, use a software package to get a computational solution and discuss the meaning and reliability of the results.

This course emphasizes applications over theory. The successful completion of the first-year course in Mathematics is required. Some computer literacy is helpful and examples/problems will be drawn from quantitative and economic courses previously attended.

We will cover the following topics:

1) R basics (installation, console, defaults, input/output)
2) Graphics, root-finding (to find, say, rates of returns or market shares and equalize marginal cost with marginal revenue)
3) Maximizers/minimizers, optimization, constrained optimization (to determine, say, optimal production, price or quantity under budget constraints)
4) State preference model and linear algebra (to spot, say, arbitrages in a simple and simplified financial market)
5) Simulation (to be used to assess a stochastic output and its variability)

Active participation is required and intense computer practice is needed to master the material and appreciate the potential of computational approaches for decision making and problem solving.

Lecture notes; commented R sessions provided by the instructor.

Suggested reading: "The R Guide" by Jason Owen, http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf (other documentation, in Englis and Italian, can be found at https://cran.r-project.org/ )

Written exam in a lab environment. More information will be provided at the beginning of the course

Lectures, practice sessions (bring your laptop with you since the first class!), and personalized exercises to be solved at home.

English

written