COMPUTATIONAL TOOLS FOR ECONOMICS

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 student must know the notions taught in the Mathematics course (first-year course). 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 fundamental and much computer practice is required to adequately implement a computational approach to 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 English and Italian, can be found at https://cran.r-project.org/ )

The assessment is based on a written and personalized exam held in a computer room, administered with the Moodle quiz mode, lasting 75 minutes.
The exam consists of a Moodle test with fully randomized questions and involves the use of R or Rstudio (chosen by the student) for the solution. The exam consists of 16 multiple-choice or single-answer questions. All questions have equal weight; 2 points are awarded for each correct answer, 0 points for incorrect or missing answers and there are no penalties for answers left blank. The exam is passed by obtaining at least 18 points.
The teacher reserves the right to request a short additional oral exam when she deems it necessary to ascertain that the student has taken the written exam appropriately, without copying or using external aids or artificial intelligence during the exam.

written

Frontal course with lectures, practice sessions (bring your laptop with you beginning with the first class!), personalized exercises to be solved at home.
The course also uses educational materials available on the university's e-learning platform moodle.unive.it.

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