Applied Analysis of Time Series and Financial Markets Data
This course is an introduction to the analysis of univariate time series data at the Bachelor level. The course will give students the theoretical knowledge and practical skills to apply the covered techniques in a wide range of empirical applications, mainly in macroeconomics (e.g., business cycle forecasting) and financial econometrics (e.g., modeling and forecasting stock market volatility).
Questions that can be addressed using the methods that this course covers are: What is the trend in a volatile sales time series? How much seasonal fluctuations do sales of a certain product have? How can we forecast next year’s GDP growth? What immediate and long-run effects does a certain shock have on another variable? How can we model and forecast the time-varying volatility of financial market returns?
The course starts with an inspection of various features of different time series with a special focus on trends and seasonal fluctuations. We will see what changes in terms of econometrics once we introduce a time dimension. Students will learn how to model time series processes using the ARMA framework. Subsequently, the course covers the basics of time series forecasting and methods for evaluating time series forecasts. We will discuss what changes if our data exhibit so-called stochastic trends and how we can test for them. Finally, we review the most important properties of financial markets data and cover the basics of GARCH models which is the most commonly used type of model to capture these properties.
This course is regularly taught in the winter semester. The course language is English.
Please note that, starting from the winter semester 2020/21, we will change the course title to “Analysis of macroeconomic and financial markets data”.
The course consists of weekly lectures (2 SWS) and exercise sessions (2 SWS). The latter focus on practicing theoretical exercises and interpretation of graphs and regression outputs and on the implementation of econometric methods in R. The lectures are taught by Prof. Dr. Jonas Dovern 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).
(Please note that a similar course was taught in German until the winter term 2018/19; if you are eligible for a re-examination please contact our secretary.)
More information about dates, grading, the software that is used, and the course in general can be found in the syllabus.