MW24.1  Empirical Methods  Master Program in Economics
 Onlineteaching in 2022/23:
 The module will be offered partially online.
 This is a course with a more technical topic.
In such a context the online format offers benefits for learning that can't be obtained with traditional lecture room formats.
Online videos allow students to follow their own learning speed.
Students can pause their video, slow down or fast forward according to their individual preferences.
Weekly online homeworks provide regular feedback and encourage students to actively engage with the material.
Online discussions and exercises provide and enhance interaction.
As a result, the online format seems to offer a much better learning experience and more room for students to interact. Students are clearly more successful than students with traditional teaching. With online teaching typically fewer than 5% of the students fail the exam. With traditional onsite teaching the number of failing students used to be much higher, typically around 25%.
I recommend that you coordinate your work with other students. Form a study group! Watch the weekly videos (or read the chapter in the handout) and then, at fixed times, exchange views with the members of your study group. Discuss: Which parts of the lecture did you and the other members of your study group find difficult? What was obvious? Perhaps you can help another student from your study group to understand a tricky concept, or perhaps someone can help you. You should also discuss your weekly homework together with the other members in your study group. Please use the discussion board in Moodle to stay in touch with the other members of the course. I will follow the discussion board attentively — you can always reach me through this channel.
 This is a course with a more technical topic.
In such a context the online format offers benefits for learning that can't be obtained with traditional lecture room formats.
Online videos allow students to follow their own learning speed.
Students can pause their video, slow down or fast forward according to their individual preferences.
Weekly online homeworks provide regular feedback and encourage students to actively engage with the material.
Online discussions and exercises provide and enhance interaction.
 Lecture:
 Oliver Kirchkamp. During the term you will in each week obtain a new set of videos. You can choose when (and how often) 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. You will find your weekly homework in Moodle. You interact with the teacher through the discussion board in Moodle.
 Exercises onsite (provided that conditions allow onsite teaching):
 Dr. Olexandr Nikolaychuk
Thu., 1618, Carl Zeiss Str. 3, SR206.
Fri., 1416, Carl Zeiss Str. 3, SR113.
Onsite exercises will start in the second week.  Exercises online:
 Dr. Olexandr Nikolaychuk
These videos are provided as an alternative to classroom teaching. Online exercises will start in the second week. Videos can be found here  Weekly homework:
 Each week you will submit a small homework (through Moodle).
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 Wed. 8.3.2023, 8:00.
The style of the questions in the take home exam on Wed. 8.3.2023, 8:00, will be similar to the questions in the weekly homework.
The sum of the points (up to 420 points) determines your grade. In this sense, the weekly homework and the take home exam constitute, pursuant to the »Prüfungsordnung«, a single partial exam.
 Discussion board:
 Please use the discussion board in Moodle to ask questions and to discuss issues related to the lecture.
 Extra video material:
 If you want to learn more about R, you might have a look at my course course Introduction to R (including videos). Here is also a course on Graphs and visualising data (including videos) with R.
 Topics:

Topic Lecture in week... Exercise in week... 1. Review of probability and statistics
(no exercise on Friday, 28.10. “Feierliche Immatrikulation”)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 1 10. Mixed effects models 1 2 11. Instrumental variables 2 3 12. Model comparison 3 4 Revision, exercises 4 5 Revision, exercises 5 6  Exam:

 You can obtain 1/3 of the total points (140 points) in the weekly homework.
 You can obtain 2/3 of the total points (280 points) in the exam on Wed. 8.3.2023, 8:00. The sum of the points in the homework and in the exam (up to 420 points) determines your grade.
 Date: Wed. 8.3.2023, 8:00 (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 us 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
 If you want to improve your routine for the exam, you find more exercises here. These exercises follow the same pattern as the homework.
 Students who do not register for the exam do not obtain any credits.
 Other material:
 Handout (contains all the slides):
 Some formulae.
 More exercises. These exercises follow the same pattern as the homework. These exercises might be useful if you want to improve your routine for the exam.
 Old exam questions (before 2020) (the style of this year's online exam questions will be similar to question from the homework. Hence, if all you want is get routine for the exam, use the homework and more exercises which follow the style of the homework (and the style of the exam). Still, the previous exams provide an extra opportunity to practice.)
 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 and R has the advantage of being free and rather powerful.
For the Bayesian parts we will use JAGS. It helps if you have installed R and JAGS on your computer when we start the course.
 Documentation for R is
provided via the built in help system but also through the
R Homepage.
Useful are
 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.
 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 commandlibrary(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 modern operating systems:
 From within R use the command
install.packages("Ecdat")
, e.g., to install the packageEcdat
 In the lecture we will use RStudio as a front end.
For the Bayesian part we will use the library
runjags
and the softwareJAGS
 Documentation for R is
provided via the built in help system but also through the
R Homepage.
Useful are
 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?