Uni Jena
Wirtschaftswissenschaftliche Fakultät
Lehrstuhl für Empirische und Experimentelle Wirtschaftsforschung
[A picture of Oliver Kirchkamp]

Lecture Econometrics IIb SS 2012

This lecture is (in SS 2012) equivalent to MW24.5 - Quantitative Economics II from the Master Program.
Lecture:
6.-10.8.2012, MPI Economics, Room V14
Audience
The lecture is primarily targeted at graduate students. Advanced students from the Hauptstudium are welcome.
Requirements:
A basic knowledge of statistical or econometric methods is helpful. In the course we will mainly put methods to use and not discuss why and when certain methods are appropriate.
Topics
  • Introduction
  • User interfaces for R
  • Syntax
  • Representing data
  • Reading data
  • Control structures
  • Functions
  • Input and output of data
  • Graphs
  • Simple Regressions
  • ...
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.
  • 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.