seit 1558

Discrete Choice

Lecture
Tuesdays, 10-12., Carl Zeiss Str. 3, SR307
Audience
The lecture is targeted at graduate students and at advanced students from the master program.
Requirements:
Econometrics I or MW 24.1 - Empirical Methods
Exam
5.8.2014, 10-12, Room 216 in the MMZ

First part: Non-linear models (8.4.-20.5.)

Topics
  • Maximum Likelihood
  • Nonlinear Regressions
  • Discrete-Choice-Models (Murray 19; Greene 23)
  • Count Data (Greene 25)
  • Survival Models (Greene 24)
Handout
If you want to prepare for the lecture (or revise), you can have a look at the Handout.
Download Discrete Choice Handout
You will find still a lot of mistakes but you might get the idea ;-)
Exercises
... can also be found in the appendix of the handout. 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 solutions to all exercises have to be submitted in time.
Literature:
  • William Greene, Econometric Analysis, Prentice Hall, 7th Edition, 2011.
  • Michael Murray; Econometrics, a Modern Introduction; Pearson 2006
  • Christian Gourieroux and Alain Monfort, Statistics and Econometric Models, Vol. 1, Cambridge, 1995.
  • John K. Kruschke , Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, 2nd Edition, 2014.

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: boot, car, Ecdat, ellipse, foreign, geepack, Hmisc, lattice, latticeExtra, lme4, lmtest, MASS, nlme, nnet, SASmixed, survival. 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.