Multivariate Time Series Analysis

Overview

This is an advanced course for Master students. It covers various aspects relevant for the analysis of multivariate time series. Multivariate time series data occurs in many areas, especially in macroeconomic (e.g., GDP, private consumption and investment for a particular country) and finance (e.g., daily returns for all stocks listed at the Frankfurt stock exchange). Often it is necessary to treat such data as being generated by a multivariate stochastic process to account for relationships that exist between the individual time series.

At the end of the course, students can explain special issues that arise when working with time series data and will know how to model multivariate dynamic processes using vectorautoregressive (VAR) models and how to forecast based on such models. Building on this, they know how to identify underlying structural shocks based on various identification methods. They can also test for spurious relationships between integrated time series and can specify and estimate models for cointegrated variables. In addition, students know the basic properties of multivariate financial market data and can specify and estimate multivariate GARCH models to capture (and forecast) time-varying volatility. They will also acquire necessary skills for using existing functions as well as for developing proprietary functions for analyzing multivariate time series in R.

Organization

We regularly teach this course in the summer semester. The course language is English.

The course can be used as a module for the following Master programs:

  • Master in Economics
  • Master in Labour Market and Human Resources
  • Master in Finance, Auditing, Controlling, Taxation (FACT)
  • Master in Marketing
  • Master in Socioeconomics
  • Master in Mathematical Economics

The course consists of weekly lectures (2 SWS) and excercise sessions (2 SWS). The latter focus primarily on the implementation of the covered methods in R.

In the summer term 2024, the lectures are taught by Dr. Monika Doll and the exercise sessions are organized by Lena Müller.

The course is worth 5 ECTS which can be earned by passing a written exam (60 minutes) at the end of the semester.

Syllabus

More information about dates, grading, the software that is used, and the course in general can be found in the syllabus.