![[A picture of Oliver Kirchkamp]](../images/oliver5344.jpeg)
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.