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, verbally describe, and quantitatively analyze economic, financial, and management 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/ ). Artificial intelligence tools will be used to generate and discuss code and check for its correctness.

a) Knowledge and understanding:
- formal definition of the mathematical problems to be used;
- introduction to generative AI;
- 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:
- the ability to understand and explain (some) relevant aspects of the problem in common language, 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)

We will discuss solutions and approaches proposed by Artificial Intelligence tools.

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.
Ökten, Giray (2019). First Semester in Numerical Analysis with Julia. Chapter 2, https://www.researchgate.net/publication/332747667_First_Semester_in_Numerical_Analysis_with_Julia
Jason Owen, "The R Guide", http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf

Personalized quiz held in the PC lab (in Mestre, Via Torino).
Assignments on moodle and posts on the forum..
Oral exam .

Details and examples of the quiz will be published on Moodle. There are from 12 to 15 questions, mainly with a numerical answer, and 3 assignments will be assessed. As a guide, each correct question is worth 2/30, the assignments/posts as a whole are worth from -2 to 4 points, and the oral exam from -2 to 2 points. A pass mark is obtained achieving at least 18/30.

written

Penalties (negative marks) for assignments and the oral exam are assigned to those who cannot civilly justify or argue for what they claim to have done personally. Those who, during the oral exam, do not demonstrate minimal proficiency (in verbally describing problems, typing functional code within a reasonable timeframe, and justifying their written work) may still fail the exam.

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

The course is taught entirely in English. If you need something, say something (paolop@unive.it). More explicitly, if you are, say, color-blind, hearing impaired, diabetic T1, disabled or differently equal in any dimension, please let me know and we will find ways to improve feasibility and interaction.