Statistics for Experimental Economists SS 2007

This course is part of the International Max Planck Research School on Adapting Behavior in a Fundamentally Uncertain World. The target group are students who, due to the interdisciplinary nature of the IMPRS school, do not have any background in statistics

Termin:

Lecture (daily): 13.8.-17.8., 8:15-9:45, 14:15-14:45

Topics:

• Introduction
• Elementary Probability Calculus
• Random Variables
• Stochastic Models and Distributions
• Limit Theorems
• Point Estimation
• Estimation of Intervals
• Basic Statistical Tests
• OLS Regression
• Maximum Likelihood (if time permits)
• Choice Models (if time permits)
• Censored Models (if time permits)

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. R is free, it is very powerful, and it is popular in the field.

• Documentation for R is provided throught the built in help. You also find support on the R Homepage. You might find the following useful:
  • The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
  • Simple R, John Verzani (Explains R with the help of examples from basic statistics)
  • Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
  • Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
  • 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.)
  • On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
• You can download R from the homepage of the R-project.

  Installing R with Microsoft Windows:

  Download and start the Installer. Install R on your local drive. Installing on a network drive or in the cloud (Dropbox, Onedrive,...) is possible but not recommended.

  Installing R with GNU-Linux:

  Follow the advice to install R for your distribution.

  Installing R with MacOS X:

  Here is a guide to install R with MacOS X.

• In the lecture we use RStudio as a front end.
• For the Bayesian parts we will use JAGS. It helps if you have installed R, RStudio, and JAGS on your computer when we start the course.
• We will use the following packages: car, Ecdat, MASS (VR), UsingR, binom, relaimpo, lmtest, mvtnorm, lattice, clinfun, memisc, xtable.

  If, e.g., the command library(Ecdat) generates an error message (Error in library(Ecdat): There is no package called 'Ecdat'), you have to install the package.

  Installing packages with Microsoft Windows:

  With RStudio: Use the tab “Install”. Otherwise: Start Rgui.exe and install packages from the menu Packages / Install Packages).

  Installing packages from GNU-Linux or MacOS X:

  From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat

Handout + Exam

The handout will be updated a few times. Data is also attached to the handout. If you wish you can have a look at the exam. I have added some notes that might help you in finding a solution.

Literature

• Stock and Watson; Introduction to Econometrics; 2nd Edition; Pearson 2006.
• Michael Murray; Econometrics, a Modern Introduction; Pearson 2006
• William Greene, Econometric Analysis, Prentice Hall, 6th Edition, 2003.
• Christian Gourieroux and Alain Monfort, Statistics and Econometric Models, Vol. 1, Cambridge, 1995.