Vorlesung Ökonometrische Verfahren (Winter 2011/12)

Diplom students can take MW24.1 - Empirical Methods as equivalent to this lecture. At the end of the lecture an exam in "Ökonometrische Verfahren" can be written.

Lecture

Fri., 10:15-11:45, Carl Zeiss Str. 3, SR128 (Oliver Kirchkamp).
The lecture on 27.1. will be swapped against the exercise on 30.1.

Exercises

Mon. 16:15-17:45, Carl Zeiss Str. 3, SR127 (Kirsten Häger).

Exam

Fr., 3.2., 10-12, SR113 (Carl Zeiss Str. 3)

Other material

• Handout (preliminary version).
• Previous exam questions

Requirements:

Basic mathematical and statistical methods as provided, e.g., in BW24.1

Topics:

• Introduction
• The classical OLS model
• Heteroscedasticity
• Irrelevant and omitted variables
• Multicollinearity
• Model selection
• Nonlinear models
• Using Maximum Likelihood
• Identification strategies

Literature:

• Stock and Watson; Introduction to Econometrics; 2nd Edition; Pearson 2006.
  • alternatively: Stock and Watson; Introduction to Econometrics; Brief Edition; Pearson 2008
• Michael Murray; Econometrics, a Modern Introduction; Pearson 2006
• Studenmund; Using Econometrics; 5th edition; Pearson 2006.
• Barreto and Howland; Introductory Econometrics; Cambridge 2006.

Examples from the lecture

In the lecture I will often use real applications as illustrations. I recommend that you try these applications 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: 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.

• Documentation for R is provided via the build in help 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.
• 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.