[推荐]学习计量的绝对好书：Applied Econometrics with R（英文原版）

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用R语言学习计量经济学的好书，希望爱好学习计量的能喜欢。

作者：Christian Kleiber, Achim Zeileis

书名：Applied Econometrics with R
出版社：Springer | 2008-08-28 | ISBN: 0387773169 | 222 pages | PDF | 3.96MB

This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.
An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/package=AER.
It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care.
The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike.

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