seit 1558
[A picture of Oliver Kirchkamp]

Resampling + Mixed Effects

This lecture is a part of MW24.3 - Quantitative Economics II from the Master Program. Ph.D. students can also obtain credits for taking this course.
Lecture:
Tue., 12:15-13:45, Carl Zeiss Str. 3, SR206
Audience
  • graduate students
  • master students in their second year
Requirements:
Econometrics I or MW 24.1 - Empirical Methods or similar.
Topics
  • Part 1: Resampling methods, bootstraps, jacknife, ... (18.10.-29.11.)
    To prepare or to revise, please have a look at the handout:
    Download Bootstrap Handout
  • Part 2: Mixed Effects Models (6.12.-31.1)
    Handout:
    Download Mixed Effects Handout.
Exercises
Exercises can be found in the handout (datasets are attached to the PDF). Participants can solve the exercises in pairs and hand in the solutions (as text or pdf) before the next lecture. No pair can hand in more than two solutions. To get a credit for the course (a part of the course) solutions to all exercises (in that part) have to be submitted in time. You can use either R (recommended) or Stata to solve the exercises.
Literature:
  • Jose C. Pinheiro and Douglas M. Bates, Mixed Effects Models in S and S-Plus. Springer, 2002.
  • Julian J. Faraway, Extending the Linear Model with R. Chapman & Hall, 2006.
  • A. C. Davison, D. V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, 1997
  • Bradley Efron and Robert J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1994
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.

For the Bayesian parts we will use JAGS. It helps if you have installed R and JAGS on your computer when we start the course.

  • 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, 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.
  • We will use the following packages: boot, bootstrap, car, Ecdat, ellipse, foreign, Hmisc, lattice, latticeExtra, lme4, lmtest, mvtnorm, MASS, nlme, survival. 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:
    Start Rgui.exe and install packages from the menu Packages / Install Packages).
    Installing packages from advanced operating systems:
    From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat
  • In the lecture we will use RStudio as a front end.