COMPUTATIONAL TOOLS FOR ECONOMICS AND FINANCE

This is a compulsory course providing notions and skills about programming and on 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/ )

In the paste, grading was based on a written personalized exam held in a lab equipped with computers. The state of prolonged covid-19 emergengy does not allow to currently clarify how the exam will be held. More information will be given at the beginning of the course or whenever possible.

Lectures (including some flipped classes), practice sessions (bring your laptop with you beginning with the first class!), personalized exercises to be solved at home. Changes are possible depending on the directives to face the covid-19 emergency.

English

Links, materials, announcements and handouts are on the elearning platform of Ca' Foscari, http://moodle.unive.it/

Accessibility, Disability and Inclusion
Accommodation and support services for students with disabilities and students with specific learning impairments

Ca' Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

written

This subject deals with topics related to the macro-area "Circular economy, innovation, work" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development