Vorlesung Ökonometrische Verfahren (Winter 2012/13)

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.

Lecturer:

Nadine Chlaß

Lecture:

As a block:

• Mo, 12.11.2012, SR 308,
  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 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.