2029 Macroeconomic Models and Methods (Applied Track)
Maximilian Böck, MSc (WU), Dr. Thomas Zörner, BA, MSc (WU)
Contact details
  • Type
  • Weekly hours
  • Language of instruction
11/20/19 to 11/29/19
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Friday 12/06/19 09:00 AM - 01:00 PM TC.3.06
Wednesday 12/11/19 03:30 PM - 07:30 PM D4.0.039
Wednesday 12/18/19 03:30 PM - 07:30 PM D4.0.127
Friday 01/10/20 09:00 AM - 01:00 PM TC.3.06
Friday 01/17/20 09:00 AM - 01:00 PM TC.3.06
Friday 01/24/20 09:00 AM - 01:00 PM TC.3.06
Friday 01/31/20 09:45 AM - 12:15 PM TC.4.03


This course develops an understanding of dynamic macroeconomics and an introduction to time series econometrics. The lectures are divided into two blocks, namely a theoretical and an empirical part. We will start with a brief introduction of business cycle stylized facts and characterizations. A detailed derivation and solution of a simple dynamical model provides an introduction to some of the tools and concepts used in modern macroeconomic analysis. Along the lines of the theoretical part, this course puts emphasis on the tools in empirical macroeconomics. Students will learn the basic tools of univariate and multivariate time series analysis with an introduction to the identification problem. The course provides thus a basis for the 2nd semester Macroeconometrics class.


Learning outcomes

The course will be helpful for students to link undergraduate macroeconomic models to more sophisticated ones. Special emphasis lies on the derivation of the models and their different solution strategies, thus providing tools for dealing with more sophisticated models. Moreover, students will gain knowledge about how to bring the models to the data for an empirical validation of the model's implications. Finally, successful participants acquire a solid knowledge for the 2nd semester course in Macroeconometrics.

Attendance requirements

Attendance is compulsory. A maximum of 4 course hours (1 day of lectures) are allowed to be missed.

Teaching/learning method(s)

The course is mainly lecture-based. Homework assignment (in groups) will be used to deepen the understanding of the materials discussed. The exercises will help the students to get familiar with the theoretical and empirical concepts discussed in the lecture. Note, that the exercises are already a preparation for the final exam. They may be prepared in groups of up to five students and are subject to a presentation in the consecutive session in order to receive some comments.

The aim of assessing class participation is to encourage students to participate in discussion, and to motivate students to engage with background reading and preparation for each session.

The final exam will cover the topics we discussed in class and will be held on 31.01.2020 at 09:00 until 11:30 in room TC 3.06.


Grading is based on:

a final exam (50 points)

homeworks (40 points)

active class participation (10 points)

Grading scheme:

<60 Points: fail; >61 Points: sufficient; >71 Points: satisfactory; >81 Points: good; >91 Points: very good.

Recommended previous knowledge and skills

Students should have a basic knowledge of statistics (probability, random variables, expectations, joint/conditional distributions), a sound knowledge of mathematics (linear algebra, differential/integral calculus, algebra, Taylor series approximation) and basic econometrics (OLS/ML estimation).

Availability of lecturer(s)

no regular office hours, please make an appointment


Last edited: 2019-07-27