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.
The e-learning platform Moodle.unive.it provides handouts, slides, exercises, and all the material needed to follow the teaching and achieve the expected learning outcomes.

Suggested reading:
"The R Guide" by Jason Owen, available for free on the web page http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf (other documentation, in English and Italian, are available for free on the web page https://cran.r-project.org/ )
Francesca Parpinel, Claudio Pizzi (2024), Dal Problema alla Soluzione: Guida Pratica per Principianti alla Programmazione in R, Giappichelli, Torino

During the course, the student is invited to complete the exercises and self-assessment tests proposed on the e-learning platform.
At the end of the course, the student will have to take the final exam.
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.
Exercises similar to those proposed in the final exam are available on the e-learning platform.
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

Evaluation grid:
30 with honors: full mastery of the topics covered in class; ability to correctly solve all the exercises proposed;
28-30: mastery of the topics covered in class; ability to correctly solve almost all the exercises proposed (14-15 exercises out of 16);
24-27: good knowledge of the topics covered in class; ability to correctly solve a good number of the exercises proposed (12-13 exercises out of 16);
20-23: knowledge of the topics covered in class that is not always complete and not always in-depth; ability to correctly solve an adequate number of the exercises proposed (10-11 exercises out of 16);
18-19: knowledge of the topics covered in class that is often superficial and sometimes incomplete; ability to correctly solve a sufficient number of the exercises proposed (9 exercises out of 16).

Frontal course with lectures and computer exercises (bring your laptop with you from the first lesson!), which also uses teaching modules available on the university e-learning platform moodle.unive.it.

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