Two - Year Master Degree in Economics

The course aims at introducing students to numerical techniques and languages for solving dynamic stochastic general equilibrium models

 The course is an introduction  to contemporary numerical methods in quantitative economics: stochastic difference equation, rational expectation equilibria, non linear models, dynamic programming, markov chains and other tools used in contemporary economics.

 The main example will be from macroeconomics and many models will be Dynamic Stochastic General Equilibrium (DSGE) models. Nevertheless, the tools taught can be used in and field of economics. The spirit of the course is to help student realizing that contemporary economics has to rely on computer power and computing methods.

 The main language  used in the application is Python, a popular open source code that is now in becoming increasingly used in economics. Knowledge of Python or any other programming language  is not essential, but some background in basic programming will be an advantage. DSGE models will be solved also with the help of Dynare, an Open suorce application For Matlab and Octave.

Students will be able to use standard open source software to solve dynamic models

The teaching will be standard in most class. Numerical examples will be provided in class

Learning will be assessed via a set of problem sets and a final exam/essay in which the students will aplly numerical techniques learnt in the course

Details of the program will be distributed at the beginning of the course. The Lectures in Quantitative Economics by Sargent, T.  and J.  Stachursky are a good exampls of some the issues that will be covered. 

Sargent, T.  and J.  Stachursky, “Quantitative Economics” freely available at www.quant-econ.net

Janui Miao  “Economics Dynamics in Discrete Time” (2014) Cambridge University Press. Mit Press