| Day | Time | Lecture Hall | Session Type |
|---|---|---|---|
| Thursday | 10:00-11:30 | XII (Main Building) | Exercises |
| Thursday | 12:00-13:30 | XII (Main Building) | Lecture |
| Friday | 10:00-11:30 | XII (Main Building) | Lecture |
Statistics for Data Analytics
Data Analytics I
Winter 2025 – University of Cologne
Organization of the Course
Statistics for Data Analytics is a graduate-level introductory course in econometrics, focusing on estimation and inference in linear models, with practical illustrations in R.
Timetable
See KLIPS for a detailed schedule.
Note: In the first session on 16 October 2025, there will be a lecture instead of exercises. The final lecture will take place on 21 November 2025.
Lecture Material
Literature
The script is self-contained. To prepare well for the exam, it’s a good idea to study this script.
The course is based on James H. Stock and Mark W. Watson’s Introduction to Econometrics (Fourth Edition). The Stock and Watson textbook is available for download: PDF by chapter (Uni Köln VPN connection required).
Further recommended textbooks are:
- Econometric Theory and Methods, by Russell Davidson and James G. MacKinnon. PDF.
- Probability and Statistics for Economists, by Bruce E. Hansen
- Econometrics, by Bruce E. Hansen
Printed versions of the books are available from the university library.
Assessment
The course will be graded by a 90-minute exam. For detailed information please visit the ILIAS course.
Communication
Feel free to use the ILIAS Statistics Forum to discuss lecture topics and ask questions. Please let me know if you find any typos in the lecture material. Of course, you can reach me via e-mail: sven.otto@uni-koeln.de
Important Dates
| Registration deadline exam 1 | November 13, 2025 |
| Exam 1 | November 27, 2025 |
| Registration deadline exam 2 | January 27, 2026 |
| Exam 2 (alternate date) | February 10, 2026 |
Please register for the exam on time. If you miss the registration deadline, you will not be able to take the exam.
R-Packages
The best way to learn statistical methods is to program and apply them yourself. Throughout this lecture script, we will use the R programming language to illustrate how econometric methods are applied in practice.
For those of you who are new to R and want to learn more about it, here’s an introductory tutorial that contains many valuable resources: rintro.svenotto.com. I also recommend the interactive R package SWIRL, which offers an excellent way to learn directly within the R environment.
To run the R code of the lecture script, you will need to install some additional packages via the command install.packages():
install.packages(c("AER", "fixest", "moments", "dynlm", "modelsummary", "scatterplot3d", "remotes"))Some further datasets are contained in my package TeachData, which is available in a GitHub repository. It can be installed using the following command:
remotes::install_github("ottosven/TeachData")