MW24.1 - Empirical Methods (2010/11)

Vorlesung

Mo, 14:15-15:45, Carl Zeiss Str. 3, SR122 (Nadine Chlaß).

Übung

Do. 12:15-13:45, Carl Zeiss Str. 3, SR125 (Nadine Chlaß)
Hier finden Sie die Übungsaufgaben

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.

Other material

• Handout (these are still in German and not complete. An updated version in English will be on the net as soon as possible).
• Previous exam questions

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 is An Introduction to R, The R language definition, Simple R, and Econometrics in R.
• A first entry into R eased through mice and menues is available through the R Commander.
• Users of Firefox get access to R help through the R Site Search Sidebar. However, this is a bit tricky. If the rsitesearch.xpi package does not install, open the package (e.g. in Emacs) and change two values in install.rdf: Set maxVersion to a version at least as large as the version of your browser. Set updateURL to an empty value.
• In the lecture I use the versatile editor Emacs with the ESS interface (ESS also helps with Stata, SAS, Splus, BUGS, and others).