COMPUTATIONAL TOOLS FOR ECONOMICS

This course provides 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 presents 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.

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) Functions, cycles (for), and conditional instructions (if) in R
4) Maximizers/minimizers, optimization, constrained optimization (to determine, say, optimal production, price, or quantity under budget constraints)
5) A basic portfolio optimization model
6) State preference model and linear algebra (to spot, say, arbitrages in a simple and simplified financial market)
7) Introduction to simulation (to be used to assess a stochastic output and its variability)
8) Introduction to the use of R for descriptive statistical analysis.
9) Use of R for linear regression

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/ )

Grading is based on a written personalized exam held in a lab equipped with computers. Due to the limited availability of computers, the exam will take place in groups of 20 students.

written

A. Grades in the range of 18-22 will be assigned in the presence of:
-Sufficient knowledge and applied understanding with reference to the program;
-Limited ability to formulate problems and implement them in R.

B. Grades in the range of 23-26 will be assigned in the presence of:
-Fair knowledge and applied understanding with reference to the program;
-Fair ability to formulate problems and implement them in R.

C. Grades in the range of 27-30 will be assigned in the presence of:
-Good/excellent knowledge and applied understanding with reference to the program;
-Good/excellent ability to formulate problems and implement them in R;
-Fully adequate communication skills.

D. Honors will be awarded for excellent knowledge, outstanding problem formulation skills in the software, and the ability to interpret and comment on the results.

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

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.

The course is carried out in collaboration with the extended partnership GRINS - Growing Resilient, INclusive and Sustainable, code PE0000018, CUP H73C22000930001, public notice no. 341/2022 of the National Recovery and Resilience Plan ("NRRP"), Mission 4 - Component 2 - Investment 1.3, funded by the European Union - NextGenerationEU.
As part of the course, meetings with companies’ testimonials involved in the project may be offered, focusing on the development of practical knowledge in the subject matter, as well as the results of the project itself.
This course covers topics related to Spoke 4 Sustainable finance - Work Package n. 3.