Lecture Econometrics I WS 2009/10

Lecturer:Dr. Nadine Chlaß

Lecture:

As a block, GK Seminar Room 214, Helmholtzweg 4 (second floor, right)

Exam:

tba

Audience

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

Requirements:

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Topics

• Econometrics and emirical economics
• OLS with one regressor
• OLS with several regressors
• Violations of the basic model
  • Heteroskedasticity
  • Autocorrelation
• Specification
• Structural breaks
• Nonlinear least squares

Handout

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Exercises

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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. R is free, it is very powerful, and it is popular in the field.

• Documentation for R is provided throught the built in help. You also find support on the R Homepage. You might find the following useful:
  • 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.
• You can download R from the homepage of the R-project.

  Installing R with Microsoft Windows:

  Download and start the Installer. Install R on your local drive. Installing on a network drive or in the cloud (Dropbox, Onedrive,...) is possible but not recommended.

  Installing R with GNU-Linux:

  Follow the advice to install R for your distribution.

  Installing R with MacOS X:

  Here is a guide to install R with MacOS X.

• In the lecture we use RStudio as a front end.
• For the Bayesian parts we will use JAGS. It helps if you have installed R, RStudio, and JAGS on your computer when we start the course.
• For R, we will use several packages. I expect that you have installed at least the following packages. runjags, AER, MASS (VR), Ecdat.

  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.

  Some packages are more optional. It might help if you have also the following packages installed: car, UsingR, binom, relaimpo, lmtest, mvtnorm, lattice, clinfun, memisc, xtable.

  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 GNU-Linux or MacOS X:

  From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat