Applied econometrics with R

Title:

Author:

Kleiber, Christian, 1966-

ISBN:

9780387773162

9780387773186

Personal Author:

Kleiber, Christian, 1966-

Publication Information:

New York : Springer, c2008.

Physical Description:

x, 221 p. : ill. ; 24 cm.

Series:

Use R!

Contents:

Introduction. -- Basics. -- Linear Regression. -- Diagnostics and alternative methods of regression. -- Models of microeconometrics. -- Time series. -- Programming your own analysis. -- References. -- Index.

Subject Term:

R (Bilgisayar program dili)

Ekonometri -- Bilgi işlem.

İstatistik -- Bilgi işlem.

R (Computer program language)

Econometrics -- Data processing.

Statistics -- Data processing.

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Library	Material Type	Item Barcode	Shelf Number	Status
Searching... Pamukkale Central Library	Book	0066067	QA276.45.R3K58 2008	Searching... Unknown

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Summary

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.

Preface	p. v
1 Introduction	p. 1
1.1 An Introductory R Session	p. 1
1.2 Getting Started	p. 8
1.3 Working with R	p. 9
1.4 Getting Help	p. 12
1.5 The Development Model	p. 14
1.6 A Brief History of R	p. 15
2 Basics	p. 17
2.1 R as a Calculator	p. 17
2.2 Matrix Operations	p. 20
2.3 R as a Programming Language	p. 22
2.4 Formulas	p. 32
2.5 Data Management in R	p. 33
2.6 Object Orientation	p. 38
2.7 R Graphics	p. 41
2.8 Exploratory Data Analysis with R	p. 46
2.9 Exercises	p. 54
3 Linear Regression	p. 55
3.1 Simple Linear Regression	p. 56
3.2 Multiple Linear Regression	p. 65
3.3 Partially Linear Models	p. 69
3.4 Factors, Interactions, and Weights	p. 72
3.5 Linear Regression with Time Series Data	p. 79
3.6 Linear Regression with Panel Data	p. 84
3.7 Systems of Linear Equations	p. 89
3.8 Exercises	p. 91
4 Diagnostics and Alternative Methods of Regression	p. 93
4.1 Regression Diagnostics	p. 94
4.2 Diagnostic Tests	p. 101
4.3 Robust Standard Errors and Tests	p. 106
4.4 Resistant Regression	p. 110
4.5 Quantile Regression	p. 115
4.6 Exercises	p. 118
5 Models of Microeconometrics	p. 121
5.1 Generalized Linear Models	p. 121
5.2 Binary Dependent Variables	p. 123
5.3 Regression Models for Count Data	p. 132
5.4 Censored Dependent Variables	p. 141
5.5 Extensions	p. 144
5.6 Exercises	p. 150
6 Time Series	p. 151
6.1 Infrastructure and "Naive" Methods	p. 151
6.2 Classical Model-Based Analysis	p. 158
6.3 Stationarity, Unit Roots, and Cointegration	p. 164
6.4 Time Series Regression and Structural Change	p. 169
6.5 Extensions	p. 176
6.6 Exercises	p. 180
7 Programming Your Own Analysis	p. 183
7.1 Simulations	p. 184
7.2 Bootstrapping a Linear Regression	p. 189
7.3 Maximizing a Likelihood	p. 191
7.4 Reproducible Econometrics Using Sweave()	p. 194
7.5 Exercises	p. 200
References	p. 201
Index	p. 213

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Table of Contents