Lecture Econometrics IIa SS 2011

Lecture:

provided as a block by N.N. ... at room 102 in the 1st floor of the Ostflügel (East-wing) Building K at Bachstraße 18

Audience

The lecture is primarily targeted at graduate students. Advanced students from the Hauptstudium are welcome.

Requirements:

Econometrics I or MW 24.1 - Empirical Methods

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. 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, 5th Edition, 2003.
• Michael Murray; Econometrics, a Modern Introduction; Pearson 2006
• Christian Gourieroux and Alain Monfort, Statistics and Econometric Models, Vol. 1, Cambridge, 1995.

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.