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logo of the Friedrich-Schiller-Universität Jena (John Frederick I, Elector of Saxony)
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[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.
Handout
Exam
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 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.