The course aims at providing base elements for computer programming on some of the most classic topics in Mathematics, Econometrics and Economics. At the end of the course, the student is going to be able to: Get the solution of ordinary differential equations; draw 2D plots, 3D surfaces and the restriction of a 3D surface on a specific plane; The student will be able to analyse and set up the pseudocoding of a problem of an economic nature involving the use of mathematical and econometric processes and to write the relevant codes.

The student will have acquired basic knowledge of programming techniques in Matlab and Python. He/she will be able to write codes concerning problems of mathematics, statistics and financial mathematics. He/she will acquire knowledge of financial models and will be able to write codes, modify and adapt existing codes to suit new needs. This knowledge will be assessed by means of classroom exercises and homework, which can be discussed with the teacher. Ability to apply knowledge and understanding The student will be able to know the syntax and structures necessary for programming; he/she will also be able to provide a pseudocoding of a series of economic, econometric and mathematical problems that are frequently repeated in the operational context. The achievement of these objectives will be assessed through the evaluation of exercises carried out in the classroom and at home, and through the evaluation of ongoing and final tests. Autonomy of judgement: the student will have developed such knowledge as to be able to assess the correctness and appropriateness of the codes developed, to be able to identify errors and to be able to resolve them.

Basic principles of computer programming: execute an instruction, iterations, conditional choices. Input and output, pseudocode. MATLAB: interface, operations on numbers, vectors, and matrices, solution of ordinary differential equations and systems, solution of unconstrained and constrained optimization problems. Python: editor, packages, base and advanced instructions.

[*] C. Pocci , G. Rotundo, R. De Kok (2016) Matlab economic and financial applications, I ed., Apogeo Education, 8891623027 / 9788891623027 [**] “Python for applications in economics and finance”. Lecture notes. [***] Slides of the lessons.

Frontal instruction; hands on computer exercises

Tests; students has to show to be able to write down the MATLAB and Python instructions that solve a specific assignment and to answer to questions on the program; eventually posed as multiple choice tests.

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The theory of differential equations and the related analyses of equilibrium, stability and long term behavior constitute the mathematical tool for the following models: - the Solow Growth Model. The model of the Nobel laureate Solow is an exogenous model of economic growth. Further developments consider the sustainablity in exhaustible resource economies - The DICE model. The Dynamic Integrated Climate-Economy model has been developed by the 2018 Nobel Laureate William Nordhaus. It integrates carbon cycle, climate science, and estimated impacts into a neoclassic economic model. The United States Environmental Protection Agency uses DICE and other models for estimating expected costs and related benefits of actions intended to slow climate change. - The Rapoport model -The Richardson model of arms' race investigates enduring rivalries between pairs of hostile powers which prompt competitive acquisition of military capability. Moreover, the course shows the Rapoport model of production and exchange, which is well suitable of extension to sustainability issues when new variables and constraints are inserted in the base formulation.