Master’s Programme in Economics. Open to other students as well.
Second autumn term, after completing Macroeconomics 1 and 2
After the course, the student should
- Understand the solution algorithms of linear rational expectation models
- Understand the basics of global solution algorithms
- Be able to understand common equilibrium concepts
- Be able to code the model with a matrix programming language
- Be able to calibrate the model
The course consists of lectures and tutorials (24 hours). The lectures and tutorials are not mandatory.
The course is passed by writing a term paper, where the student calibrates an existing model and uses it to study a policy question.
|Edeltävät opinnot tai edeltävä osaaminen
The prerequisite is sufficient knowledge of time series analysis (Econometrics 1 or Advanced Econometrics 1 and 2, and Advanced Econometrics 3) and macroeconomic theory (Macroeconomics 2 or two modules of Advanced Macroeconomics courses).
|Suositeltavat valinnaiset opinnot
Any of the following courses will help in learning the course material: Macroeconometrics, Bayesian Econometrics, Money and Monetary Theory, Open Economy Macroeconomics. These are not required.
The goal of the course is to provide an introduction to the methods of modern applied, quantitative macroeconomics. The course builds on existing dynamic stochastic general equilibrium (DSGE) models. The aim is to learn to solve the model and to calibrate the model parameters, and to apply them in analyzing macroeconomic questions.
|Oppimateriaali ja kirjallisuus
In addition to the lecture material, selected parts (covered in the course) of David DeJong and Chetan Dave (2011): Structural Macroeconometrics, 2nd ed., Princeton University Press and of Miranda and Fackler (2002) Applied Computational Economics and Finance, are recommended.
|Oppimista tukevat aktiviteetit ja opetusmenetelmät
All course material is delivered through the course website/Moodle.
|Arviointimenetelmät ja -kriteerit
The grade on a scale from 0 (fail) to 5 is based on the points earned in the term paper.