MW24.1 - Empirical Methods - Master Program in Economics

Online-teaching in 2021/22:
As in the previous year, the module will be offered online.
  • Teaching needs a stable and reliable environment. We must commit to a format that works well under many forseeable conditions. Quality suffers once teaching follows erratic changes of daily political assessments.
  • Online teaching works very well in this field. In old-fashioned lecture rooms you would sit, together with a large group of other students, far away from the blackboard. I do like a lively audience, but in the traditional lecture room it is hard for a large group of students to follow the course and hard to ask questions. Videos allow you to find your own personal learning speed. Weekly online exercises provide regular feedback. A discussion board and Q&A sessions give you the opportunity to interact and to sort out difficulties you have with the material of the course.

    As a result, students with online teaching learn better and are clearly more successful in the exam than students with traditional teaching.

  • I do care about interaction with students. Keep in mind that interaction can work very well in an online format. This is a lecture with a large number of participants and with a rather technical topic. In such a context a predominantly 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. I also plan synchronous Q&A sessions.
Exercises:
Dr. Olexandr Nikolaychuk The exercises will be provided online and start in the second week. You will receive videos for each week.
Q+A:
Thursdays, 12:15-13:45. You find the Zoom access in Moodle. To use the Q+A in a productive way, you should be already familiar with the content of the lecture and the exercises.
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., 16. 2. 2022, 11:00-12:00. The sum of the points (up to 420 points) determines your grade. The style of the questions in the exam on Wed., 16. 2. 2022, 11:00-12:00, will be similar to the homework.
Discussion board:
Please use the discussion board in Moodle to ask questions and to discuss issues related to the lecture.
Topics:
TopicLecture and Q+A in week...Exercise in week...
1. Review of probability and statistics4243
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
Summary, revision45
Summary, revision56
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., 16. 2. 2022, 11:00-12:00. The sum of the points in the homework and in the exam (up to 420 points) determines your grade.
  • Date: Wed., 16. 2. 2022, 11:00-12:00 (online, participants will receive an email explaining which online exam room to use several days before the exam).
  • 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
  • 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.
  • Previous exam questions (the type of the online exam will be similar to the homework. Still, the previous questions should provide a good exercise.)
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: