Empirical Methods
 The module provides advanced methods in empirical economics. A rigorous and quantitative understanding of our environment is a necessary requirement to assess economic activities and their environmental consequences. Empirical inference is essential to understand the effectiveness and the cost and the benefits of policies. An empirical model also helps predicting the development and the dynamics of an environment over time and, thus, help us make informed decisions.
 Onlineteaching:
 The module will be offered online.
This is a course with a more technical topic. For this course the online format offers benefits for learning that we miss in a traditional lecture room. Online videos allow you to follow your own learning speed. You can (and you should) pause your video, slow down or fast forward according to your individual learning speed. Weekly online homeworks give you regular feedback and help you to engage with the material. Online discussions and exercises provide and enhance interaction.
As a result, the online format gives you a much better learning experience and more room to interact. For this course, students are clearly more successful with online teaching than students with traditional teaching. In the past, with traditional classroom teaching, about 25% of the students failed the course. Now, with online teaching, fewer than 5% of the students fail.
 Lecture + Exercises:
 Oliver Kirchkamp. During the term you will in each week obtain a new set of videos. You can choose when (and how) you watch these videos. These videos will remain available until the end of the term. I recommend to follow a routine: Watch the weekly videos on always the same day at always the same time. Each week you will also complete a small homework.
 Weekly homework:
 Each week you will submit a small homework (through Moodle).
Although different students will work on different problems, it will be useful to discuss your homework in your study group. You should use the discussion board in Moodle to ask questions and to stay in touch with the other members of the course.
You can obtain 1/3 of the total points (140 points) with the homework. You can obtain 2/3 of the total points (280 points) in the exam on 23.2.2024, 8:30.
At the end of the term, you will receive extra (optional) problem sets. These problem sets are similar in style to the exam. If you want to improve your routine for the exam, you can try these problems as often as you like. However, these problems don't count for your grade.
 Discussion board + Online Meeting:

Please use the discussion board in Moodle to ask questions and to discuss issues
related to the lecture. I try to answer your questions as soon as possible, usually within one working day.
You find the access code for the online meeting Moodle. Before joining the meeting, you should have watched the videos and you should have made an attempt to solve the homework. Please come in time and, if possible, activate your camera. I don't plan to introduce new material in the discussion board or online meeting.
 Exam:

 You obtain up to 1/3 of the total points (140 points) in the weekly homework.
 You obtain up to 2/3 of the total points (280 points) in the exam on 23.2.2024, 8:30, . The style of the questions in the take home exam on 23.2.2024, 8:30, will be similar to the questions in the weekly homework.
The sum of the points (up to 420 points) determines your grade. I assume that, pursuant to the »Prüfungsordnung«, the weekly homework and the take home exam constitute a single partial exam.
 Date: 23.2.2024, 8:30 (online). You can write the exam at home — provided you have a good connection to the internet. If you prefer to write at the FSU Jena, please let me know.
 Resit of the final exam: t.b.a. (online).
Students who want to take the resit of the final exam must register in time with the examination office.
 Instructions for the exam
 Students who do not register for the exam do not obtain any credits.
 Topics:

Topic Lecture in week... Exercise in week... 1. Review of probability and statistics 42 43 2. Review of frequentist inference 43 44 3. Review of linear Regression 44 45 4. Review of models with multiple regressors 45 46 5. Bootstrap 46 47 6. Bayesian methods 47 48 7. Bayes in practice 48 49 8. Binary choice 49 50 9. More on discrete choice 50 51 10. Mixed effects models 51 2 11. Instrumental variables 2 3 12. Model comparison 3 4 Revision, exercises 4 5 Revision, exercises 5 6  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, 2012.
 James H. Stock and Mark W. Watson. “Introduction to Econometrics”. Pearson, 2011.
 John Kruschke. “Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan”. Academic Press, 2nd Edition, 2014.
 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")
from now on all commands refer to this dataset, e.g.:
attach(Caschool)plot(str,testscr)
, or
large<factor(str>20)
A brief documentation of the variables of this dataset can be obtained with
t.test(testscr~large)
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 Rproject.
 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 GNULinux:
 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.
 We will use the following packages:
AER, boot, bootstrap, calibrate, car, coda, Ecdat, geepack, Hmisc, lattice, latticeExtra, lme4, lmtest, MASS, memisc, nnet, plotrix, plyr, relaimpo, runjags, Sleuth2
.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. Installing packages with Microsoft Windows:
 With RStudio: Use the tab “Install”. Otherwise: Start
Rgui.exe
and install packages from the menuPackages / Install Packages
).  Installing packages from GNULinux or MacOS X:
 From within R use the command
install.packages("Ecdat")
, e.g., to install the packageEcdat
For the Bayesian part we will use the library
runjags
and the softwareJAGS
 Documentation for R is provided throught the built in help. You also find support on the R Homepage.
You might find the following useful:
 FAQ:
 I have a question regarding the lecture?
 The term has already started. Can I still join the course?
 I realise I have not enough time for the course. How can I leave?
 Which questions should be asked in consultation hours?
 I need a letter of recommendation.
 Lecture, Exercises, Handout, Discussion board, Homework...  this is too much!
 Why can't all videos have the same length?
 Do I have to take the homework in the same term as the exam?
 How much time do I have for each homework?
 Is it possible to install R and RStudio on a network filesystem (e.g. Microsoft OneDrive)?
 How should I enter decimal numbers in Moodle?
 Moodle complains about “incomplete answers”.
 How does Moodle grade answers to the homework?
 I could not submit the homework in time. Can I still get credits for my homework?
 How hard is the exam?
 How do I register for the exam?