Applied Econometrics with R

1. Front Matter

   Pages 1-9

   PDF

2. Introduction

   • Christian Kleiber, Achim Zeileis

   Pages 1-16

3. Basics

   • Christian Kleiber, Achim Zeileis

   Pages 1-38

4. Linear Regression

   • Christian Kleiber, Achim Zeileis

   Pages 1-38

5. Diagnostics and Alternative Methods of Regression

   • Christian Kleiber, Achim Zeileis

   Pages 1-27

6. Models of Microeconometrics

   • Christian Kleiber, Achim Zeileis

   Pages 1-30

7. Time Series

   • Christian Kleiber, Achim Zeileis

   Pages 1-31

8. Programming Your Own Analysis

   • Christian Kleiber, Achim Zeileis

   Pages 1-18

9. Back Matter

   Pages 1-21

   PDF

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

• Analysis
• STATISTICA
• Statistical Computing
• econometrics
• optimization
• programming
• sets
• simulation
• quantitative finance

Researchers in quantitative social sciences in general, and econometrics in particular, have often favored scripting languages such as GAUSS or Stat, or packages such as EViews. Introducing R to this particular audience could therefore be a well-appreciated title among the growing number of publications about R…. So, is this a good introduction of R for econometricians? Absolutely— with a well-rounded selection of available methodologies, both classic and current, and a good focus on introducing graphical methods, as well as gently covering more novel and therefore less familiar approaches, it fulfills its task with aplomb. The writing style is conversational without being shallow. (Dirk Eddelbuettel, Journal of Statistical Software, February 2009, Vol. 29, Book Review 14)

• Wirtschaftswissenschaftliches Zentrum , (WWZ), Universität Basel, Basel, Switzerland

  Christian Kleiber

• Inst.für Statistik, TU Wien, Wien, Austria

  Achim Zeileis

Policies and ethics