Lecture Econometrics III WS 2010/11

This lecture is (in 2010/11) equivalent to MW24.3 - Quantitative Economics II from the Master Program. During the first half of the term we will talk about bootstraps. GK-EIC students who take only this part of the course obtain 3 credits. During the second half of the term we will talk about mixed effects. GK-EIC students who take only the second part of the course obtain 3 credits. GK-EIC students who take both parts of the course obtain 6 credits.

Lecture:

Tue., 12:15-13:45, Carl Zeiss Str. 3, SR129

Audience

• graduate students
• master students in their second year
• advanced students from the Diplom

Requirements:

Econometrics I or MW 24.1 - Empirical Methods or similar.

Topics

• Part 1: Resampling methods, bootstraps, jacknife, ... (19.10.-30.11.)
  Handout in A4 and in A5.
• Part 2: Mixed Effects Models (7.12.-8.2)
  Handout in A4 and in A5.

Exercises

Exercises can be found in the handout (datasets are attached to the PDF). Participants can solve the exercises in pairs and hand in the solutions (as text or pdf) before the next lecture. No pair can hand in more than two solutions. To get a credit for the course (a part of the course) solutions to all exercises (in that part) have to be submitted in time. You can use either R (recommended) or Stata to solve the exercises.

Literature:

• Jose C. Pinheiro and Douglas M. Bates, Mixed Effects Models in S and S-Plus. Springer, 2002.
• Julian J. Faraway, Extending the Linear Model with R. Chapman & Hall, 2006.
• A. C. Davison, D. V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, 1997
• Bradley Efron and Robert J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1994

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.

• 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, tries to explain R with the help of examples from basic statistics)
  • Simple R, John Verzani (Tries to explain R with the help of examples from basic statistics)
  • Einführung in R, Günther Sawitzki (In German. Rather compact introduction. The statistical part can be quite demanding)
  • Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic. The econometric models go beyond what we are doing in this lecture)
  • 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. A must to read if you write your own functions)
• A first entry into R eased through mice and menues is available through the R Commander.
• An interesting development environment is RStudio.
• In the lecture I use the versatile editor Emacs with the ESS interface (ESS also helps with Stata, SAS, Splus, BUGS, and others). Users of MacOS-X will prefer the Emacs-Clone aquamacs.