This course provides an applied perspective on univariate and multivariate time series analysis. It begins by briefly reviewing univariate time series models such as autoregressive moving average (ARMA) models. The course then moves on to the analysis of multivariate time series models, such as vector autoregressive (VAR) models, and their extensions to incorporate structural characteristics, such as structural vector autoregressive (SVAR) models. The course covers the estimation routines for these models and discusses the problem of identifying the nature of structural shocks, including short- and long-run restrictions and sign restrictions. The course also introduces the Bayesian paradigm in econometrics and compares it to the frequentist approach. In the end, students will be able to use structural tools to derive recommendations for policymakers and to conduct their own small research projects applying time series analysis.

This course is designed for students interested in working at research institutions or financial institutions, and covers the most important tools used in applied time series analysis. Rather than focusing narrowly on the application of econometric tools in macroeconomics, the course aims to provide a deeper understanding of these tools, their proper use and their limitations, illustrated by applications to questions considered in macroeconomics. The methods discussed in the course, such as univariate and multivariate time series models, are used heavily in central banks and policy institutions and will be covered with a special emphasis on their applications and interpretations. By the end of the course, students will be able to conduct their own small research projects applying time series analysis.

Attendance is mandatory for this course. However, you are allowed to miss a maximum of one meeting. 

This lecture consists of two main blocks. While in the first block we are discussing the topics mentioned in the syllabus (slides, literature, and papers will be provided), the second block is dedicated to students' presentations of famous examples in the literature.  The group (max. 5 students) is expected to scrutinize the paper in depth (objective, relevant assumptions, model framework, and results) and provide (a) a detailed discussion as well as (b) potential comments/questions/suggestions to the authors. There will be sufficient time for a deeper discussion.

Please make sure that you read the assigned literature PRIOR to the lecture.

The course grade will be based on the following components:

• Final exam (40 points)
• Paper presentation (30 points)
• Exercises (30 points)

To pass the course, a positive final exam score (50% or higher of total exam points) is required. The grading key is as follows:

• Less than 60 points: fail (5)
• 61-70 points: sufficient (4)
• 71-80 points: satisfactory (3)
• 81-90 points: good (2)
• More than 91 points: very good (1)

The final exam will consist of a mix of multiple-choice and open-ended questions, and will last for 90 minutes. It is currently planned to take place in person on campus.

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To succeed in this course, students should have a strong foundation in statistics (including probability, random variables, expectations, and joint/conditional distributions), mathematics (such as linear algebra, calculus, and algebra), and basic econometrics (including OLS and maximum likelihood estimation). While the course will cover univariate time series, students should have a basic understanding of this topic before enrolling in the course.

tzoerner@wu.ac.at