MW24.1 - Empirical Methods - Master Program in Economics

Online-teaching in 2022/23:
The module will be offered partially online.
  • In this field, online teaching is more effective than traditional teaching. In a traditional lecture large groups of students sit far away from the blackboard. Students in such a classroom who find the professor's monologue too fast can only try to copy the entire monologue and hope to understand later. Student who find the monologue too slow need patience. This is not an environment for an inspired discussion.

    Online videos allow you to follow your own learning speed. You can slow down the playback or fast forward according to your own preferences. Weekly online homeworks encourage you to actively engage with the material and provide regular feedback. A discussion board and exercises allows you interact with a teacher and with other students after you had a chance to understand the material.

    As a result, students learn better if we support them with online teaching. Students are clearly more successful in the exam than students with traditional teaching. With on-site teaching about 25% of the students failed the exam. With on-line teaching fewer than 5% fail.

  • I do care about interaction with students. However, the traditional professor's monologue in the classroom is not really interactive teaching. The online format offers a better learning experience and more room to interact. This is a lecture with a rather technical topic. In such a context the online format offers benefits for learning that can't be obtained with old-fashioned lecture room formats.

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.

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 on-site (provided that conditions allow on-site teaching):
Dr. Olexandr Nikolaychuk
Thu., 16-18, Carl Zeiss Str. 3, SR206.
Fri., 14-16, Carl Zeiss Str. 3, SR113.
On-site exercises will start in the second week.
Exercises online:
Dr. Olexandr Nikolaychuk
These videos are provided as an alternative to classroom teaching. On-line 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.
Topics:
TopicLecture in week...Exercise in week...
1. Review of probability and statistics
(no exercise on Friday, 28.10. “Feierliche Immatrikulation”)
4243
2. Review of frequentist inference4344
3. Review of linear Regression4445
4. Review of models with multiple regressors4546
5. Bootstrap4647
6. Bayesian methods4748
7. Bayes in practice4849
8. Binary choice4950
9. More on discrete choice501
10. Mixed effects models12
11. Instrumental variables23
12. Model comparison34
Revision, exercises45
Revision, exercises56
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.
  • Here is further information for students who took parts of the course before 2020/21.
  • 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")
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 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 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 menu Packages / Install Packages).
    Installing packages from modern operating systems:
    From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat
  • In the lecture we will use RStudio as a front end.

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

FAQ: