| Registration deadline exam 1 | Jul 04, 2024 |
| Exam 1 | Jul 18, 2024, 16:00-17:30 |
| Registration deadline exam 2 | Aug 13, 2024 |
| Exam 2 (alternate date) | Aug 20, 2024, 11:30-13:00 |
Empirical Methods
1314MAEMT1 – AM Empirical Methods – University of Cologne
Welcome to the course!
Empirical Methods is a graduate-level course in regression analysis focusing on specialized econometric tools. We cover advanced topics such as panel data methods, generalized linear models, high-dimensional regression, instrumental variables, causal inference, and time series regression. Emphasis is on both theoretical understanding of the methods and practical applications using the R programming language.
Course Materials
This webpage and its pdf version: the online script
eWhiteboard: the whiteboard notes
ILIAS: further course material
RScripts: codes from the lecture
Literature
The course is primarily based on the following textbook:
- Stock, J.H. and Watson, M.W. (2019). Introduction to Econometrics (Fourth Edition). Pearson.
The Global Edition of Stock and Watson (2019) is available here. To view the book, please activate your Uni Köln VPN connection.
Recommended reading to accompany the lecture:
| Part | Reading |
|---|---|
| A – Basic Principles | Stock and Watson: Sections 1–3 |
| B – Linear Regression | Stock and Watson: Sections 4–9 and 18–19 |
| C – Panel Data Methods | Stock and Watson: Section 10 |
| D – Big Data Econometrics | Stock and Watson: Section 14 |
| E – Time Series Methods | Stock and Watson: Section 15 |
For specialized topics beyond Stock and Watson (2019), the following textbooks are recommended:
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2019). An Introduction to Statistical Learning with Applications to R (Second Edition). Springer.
Davidson, R., and MacKinnon, J.G. (2004). Econometric Theory and Methods. Oxford University Press.
James et al. (2019) is available for free here and here. Davidson and MacKinnon (2004) is available for free on the author’s webpage: LINK. Printed versions of the books are available from the university library.
Preparation
You should also be familiar with the basic concepts of matrix algebra and probability theory. Please consider the following refreshers:
Crash Course in Matrix Algebra
Probability Theory for Econometricians
We will be using the statistical programming language R. Please make sure you have R and RStudio installed before the class. Here you find the installation instructions for the software. If you are a beginner, please consider this short introduction, which contains many valuable resources:
Assessment
The course will be graded by a 90-minute exam. More information about the assessment can be found on ILIAS.
Communication
Feel free to use the ILIAS methods forum to discuss lecture topics and ask questions. Please also let me know if you find any typos. Of course, you can reach me via e-mail: sven.otto@uni-koeln.de
Important Dates
Please register for the exam on time. If you miss the registration deadline, you will not be able to take the exam (the Examinations Office is very strict about this). You only need to take one of the two exams to complete the course. The second exam will serve as a make-up exam for those who fail the first exam or do not take the first exam.
Timetable
The course is held on Tuesdays from 14:00 to 15:30 in Hörsaal XXI and on Thursdays every two weeks from 16:00 to 17:30 in Hörsaal VI: KLIPS TIMETABLE.
R-Packages
To run the R code of the lecture script, you will need to install some additional packages.
install.packages(
c("AER", "plm", "dynlm", "glmnet", "moments", "urca",
"tidyverse", "stargazer", "BVAR",
"palmerpenguins", "kableExtra", "scatterplot3d"))Some further datasets are contained in my package teachingdata, which is available in a GitHub repository:
install.packages("remotes")
remotes::install_github("ottosven/teachingdata")See the Ilias course on how to install teachingdata2.