MW24.1 - Empirical Methods - Master Program in Economics

Online-teaching in 2020/21:
To protect you and your contacts during the SARS-CoV-2 pandemic, the module will be offered online. I hope that this measure contributes to the joint effort of keeping case numbers during the SARS-CoV-2 pandemic low. I hope that we can all help reducing the strain on our health system and avoid unnecessary contacts. We may find it difficult to change our daily routines for studies and for free time. I do hope that, nevertheless, with some creativity we will be able to find good and safe alternatives that help us with our everyday tasks and that help reducing the dangers of SARS-CoV-2 for us and for other people. I am grateful for your contribution to this joint effort.

I recommend that, in particular during the SARS-CoV-2 pandemic, you learn together with other students. Form a virtual study group. Coordinate your work with other students. You can watch the weekly videos and then, at fixed times, exchange views with the members of your virtual 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 virtual 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. You will receive videos for each week. You find these videos in the table of topics below. Of course, you can choose when (and how often) to watch these videos. I suggest you watch the videos each week at a fixed time. You will also complete a (small) weekly homework. You find the homework in moodle. You can interact with the teacher through the discussion board in moodle.
Exercises:
Dr. Olexandr Nikolaychuk The exercises will be provided online. You will receive videos for each week.
Weekly homework:
Each week you will submit a small homework (through moodle). The homework counts for 1/3 of your grade. The exam at the end of the term counts for 2/3 of your grade. The style of the questions in the final exam will be similar to the homework.
Discussion board:
Please use the discussion board of this course to ask questions and discuss issues related to the lecture.
Topics:
WeekTopic
45Review of probability and statistics
46Review of frequentist inference
47Review of linear Regression
48Review of models with multiple regressors
49Bootstrap
50Bayesian methods
51Bayes in practice
1Binary choice
2More on discrete choice
3Mixed effects models
4Instrumental variables
5Model comparison
6Summary, revision
Exams:
To get a credit for the course students have to complete weekly homeworks (1/3 of the total grade) and the final exam (2/3 of the total grade).
  • Candidates who have before 2020/21 passed the final but not the midterm have two options. In any case they must register for this year's exam:
    • They can have an oral exam as a replacement for the midterm. Their final grade will then be 1/3 of the oral exam plus 2/3 of their old final exam. If you want to take this option, please let us know until 1st December.
    • They can just take the final exam this year. Their final grade will then be 1/3 of the homework this year plus 2/3 of their final exam this year.
  • Candidates who have (before 2020/21) passed the midterm but not the final should submit the homework this year and should take the final exam. I will then calculate the grade from 1/3 of the homework + 2/3 of the final exam. Let us call this grade X.
    • If grade X is better than their old grade from the midterm, they will keep X as a grade.
    • If the old grade from the midterm is better than X, then the midterm will count for 1/3 and X will count for 2/3 of their final grade.

      I hope that this arrangement provides a fair solution for all students who did parts of the course before.

Students who do not register for the exam do not obtain any credits.
Final exam (2/3):
t.b.a.

You must register in Friedolin for the exam (not simply for the course), even if you have passed the midterm earlier.

Other material:
  • Handout (contains all the slides):
  • 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:
    Start Rgui.exe and install packages from the menu Packages / Install Packages).
    Installing packages from advanced 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