Oliver Kirchkamp

Empirical Methods

The module provides advanced methods in empirical economics. A rigorous and quantitative understanding of our environment is a necessary requirement for assessing economic activities and their environmental consequences. Empirical inference is essential to understanding the effectiveness, costs, and benefits of policies. Furthermore, an empirical model helps predict the development and dynamics of an environment over time, thus aiding informed decision-making.

Online-teaching:

The module will be offered online.

This course covers a more technical topic. For this course, the online format offers learning benefits that are absent in a traditional lecture room. Online videos allow you to learn at your own pace. You can (and you should) pause, slow down, or fast forward according to your individual learning speed. Weekly online homework assignments provide regular feedback and help you to engage with the material. Online discussions and exercises provide and enhance interaction.

Consequently, the online format offers a significantly better learning experience and more opportunities to interact. For this course, students are demonstrably more successful with online teaching than students with traditional teaching. In the past, with traditional classroom teaching, approximately 25% of the students failed the course. Now, with online teaching, fewer than 5% of the students fail.

Lecture + Exercises:

Throughout the term, you will receive a new set of videos each week. You can choose when (and how) you watch these videos. These videos will remain available until the end of the term. I recommend that you establish a routine: watch the weekly videos on the same day and at the same time every week. Each week, you will also complete a short homework assignment.

Weekly Digital Homework:

Each week, you will submit a short homework assignment via Moodle. Although different students will work on different problems, discussing your homework within your study group will be beneficial. Use the discussion board in Moodle to ask questions and to keep in touch with the other course members.

You can obtain one-third of the total points (140 points) with the homework.

At the end of the term, you will find extra (optional) problem sets in Moodle. These extra problem sets are similar in style to the exam. You may attempt these problem sets as often as you wish. For each attempt, you will receive a new collection of problems. When you submit your answers to these extra questions, you will receive immediate feedback and some comments. You can use these extra questions to become more accustomed to the exam. Points scored with these extra questions do not contribute to your final grade.

Digital Exam:

You can obtain up to two-thirds of the total points (280 points) in the digital exam (on Moodle) on 17.02.2027. The style of the questions in the digital exam on 17.02.2027, will be similar to the questions in the weekly homework. If you have a reliable internet connection, you can write the digital exam from home. If you prefer to write at FSU Jena and require a room, please let us know.
The sum of the points from the digital homework and the digital exam (up to 420 points) determines your grade.
Date of the digital exam: 17.02.2027 (on Moodle).
Resit of the digital exam: 19.04.2027 (on Moodle).
Students who wish to take the resit of the digital exam must register in time with the examination office.
Instructions for the Digital Exam
If you wish to obtain credits for the course, please do not forget to register for the exam!

Discussion Board and Online Meeting:

Use the discussion board in Moodle to ask questions and to discuss issues related to the lecture. I will endeavour to answer your questions as soon as possible, usually within one working day.

The access code for the online meeting can be found in Moodle. I recommend that, before joining the meeting, you watch the videos and attempt to solve the homework. Please arrive on time, and if possible, activate your camera. I do not plan to introduce new material in the discussion board or during the online meeting.

Topics:

Topic	Lecture/Exercise
1. Review of probability and statistics	19.10.2026 - 25.10.2026
2. Review of frequentist inference	26.10.2026 - 01.11.2026
3. Review of linear Regression	02.11.2026 - 08.11.2026
4. Review of models with multiple regressors	09.11.2026 - 15.11.2026
5. Bootstrap	16.11.2026 - 22.11.2026
6. Bayesian methods	23.11.2026 - 29.11.2026
7. Bayes in practice	30.11.2026 - 06.12.2026
8. Binary choice	07.12.2026 - 13.12.2026
9. More on discrete choice	14.12.2026 - 03.01.2027
10. Mixed effects models	04.01.2027 - 10.01.2027
11. Instrumental variables	11.01.2027 - 17.01.2027
12. Model comparison	18.01.2027 - 24.01.2027
Homework, extra problems, discussion forum, no videos with new material	25.01.2027 - 31.01.2027
Homework, extra problems, discussion forum, no videos with new material	01.02.2027 - 07.02.2027
Homework, extra problems, discussion forum, Q+A	08.02.2027 - 14.02.2027

Other material:

Handout (contains all the slides):
Some formulae.

Requirements:

Basic mathematical and statistical methods as, e.g., in BW24.1.

Literature:

To learn more, I recommend the following textbooks. You find further recommendations at the end of each chapter in the handout. (If you find not enough copies of the books you need in the library, please tell the librarians. If you do not tell them, nothing will change.)

William H. Greene. “Econometric Analysis”. Pearson, 2018.
James H. Stock and Mark W. Watson. “Introduction to Econometrics”. Pearson, 2018.
Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer. “Introduction to Econometrics with R”. 2026.
John Kruschke. “Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan”. Academic Press, 2nd Edition, 2014.
Jim Albert and Jingchen Hu, Probability and Bayesian Modeling, 2020.

Examples from the lecture:

In the lecture I will often use practical examples as illustrations. I recommend that you try these examples on your own. To do this, open R and type the following:


data(Caschool,package="Ecdat")

attach(Caschool)

from now on all commands refer to this dataset, e.g.: plot(str,testscr), or

large<-factor(str>20)

t.test(testscr~large)

A brief documentation of the variables of this dataset can be obtained with help(Caschool)

Software:

For our practical examples (during the entire course) we will use the software environment R. I think that it is helpful to coordinate on one environment. R is free, it is very powerful, and it is popular in the field.

Documentation for R is provided throught the built in help. You also find support on the R Homepage. You might find the following useful:
- The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
- Simple R, John Verzani (Explains R with the help of examples from basic statistics)
- Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
- Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
- An Introduction to R, W. N. Venables und D. M. Smith (The focus is more on R as a programming language)
- The R language definition (Concentrates only on R as a programming language.)
- On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
You can download R from the homepage of the R-project.

Installing R with Microsoft Windows:

Download and start the Installer. Install R on your local drive. Installing on a network drive or in the cloud (Dropbox, Onedrive,...) is possible but not recommended.

Installing R with GNU-Linux:

Follow the advice to install R for your distribution.

Installing R with MacOS X:

Here is a guide to install R with MacOS X.
In the lecture we use RStudio as a front end.
For the Bayesian parts we will use JAGS. It helps if you have installed R, RStudio, and JAGS on your computer when we start the course.
For R, we will use several packages. I expect that you have installed at least the following packages. runjags, AER, MASS (VR), Ecdat.
If, e.g., the command library(Ecdat) generates an error message (Error in library(Ecdat): There is no package called 'Ecdat'), you have to install the package.
Some packages are more optional. It might help if you have also the following packages installed: car, UsingR, binom, relaimpo, lmtest, mvtnorm, lattice, clinfun, memisc, xtable.
Installing packages with Microsoft Windows:
- With RStudio: Use the tab “Install”.
- Otherwise: Start Rgui.exe and install packages from the menu Packages / Install Packages).
Installing packages from GNU-Linux or MacOS X:

From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat

For the Bayesian part we will use the library runjags and the software JAGS

FAQ:

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