An Introduction To Econometrics
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Applied Econometrics with R
Author: Christian Kleiber
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-12-10
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.
Introduction to Econometrics
Author: Christopher Dougherty
language: en
Publisher: Oxford University Press, USA
Release Date: 2011-03-03
Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible.
An Introduction to Econometrics
An introductory textbook (requiring no previous knowledge of probability and statistics) that offers students a solid foundation in regression analysis. This unique introduction to econometrics provides undergraduate students with a command of regression analysis in one semester, enabling them to grasp the empirical literature and undertake serious quantitative projects of their own. It does not assume any previous exposure to probability and statistics but does discuss the concepts in these areas that are essential for econometrics. The bulk of the textbook is devoted to regression analysis, from simple to advanced topics. Students will gain an intuitive understanding of the mathematical concepts; Java applet simulations on the book's website demonstrate how the algebraic equations are derived in the text and are designed to reinforce the important concepts. After presenting the essentials of probability and statistics, the book covers simple regression analysis, multiple regression analysis, and advanced topics including heteroskedasticity, autocorrelation, large sample properties, instrumental variables, measurement error, omitted variables, panel data, simultaneous equations, and binary/truncated dependent variables. Two optional chapters treat additional probability and statistics topics. Each chapter offers examples, prep problems (bringing students "up to speed" at the beginning of a chapter), review questions, and exercises. An accompanying website offers students easy access to Java simulations and data sets (available in EViews, Stata, and Excel files). After a single semester spent mastering the material presented in this book, students will be prepared to take any of the many elective courses that use econometric techniques. * Requires no background in probability and statistics * Regression analysis focus * "Econometrics lab" with Java applet simulations on accompanying Website