Applied Macroeconomics

ECOM-410 (MSc) / DPE-9410 (PhD)

In case of conflicting information consider the Sisu/Courses/Moodle pages the primary source of information.

Overview

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.

Teaching

  • Completion method: contact teaching
  • Schedule: can be found in Course Page and Sisu
  • Study materials: can be found in Moodle
    • A link and a Moodle course key will be sent by email before the course starts and/or they will be provided on the Courses page: you can view the information on this site without logging in or registering, but some of the content added by teachers to course pages may be available to course participants only, for example Moodle course enrolment key.
    • Log in with your UH username to be able to use all the features of the course workspace

Please register for the course in the UH Sisu with your UH username. Further instructions (link to be added here later).

University-specific instructions

    • Code: no equivalent code

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Credit transfer: apply for inclusion in Sisu

    Further instructions (link to be added here later)

    • Code: 26065

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Credit transfer: apply for substitution in Sisu

    Further instructions (link to be added here later)

    • Code: ECOM-410 (rMSc) / DPE-9410 (PhD)

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Please contact your supervisor/program director to be sure that the course credit can be counted towards your degree

    • Credit transfer: please apply for credit transfer according to your home university’s procedures

    Further instructions (link to be added here later).Further instructions (link to be added here later).

Learning outcomes

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