Advances in Social Science Research Using R 2010年新书

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[size=120%]Advances in Social Science Research Using R (Lecture Notes in Statistics / Lecture Notes in Statistics - Proceedings)
By Hrishikesh D. Vinod

Publisher: Springer
Number Of Pages: 205
Publication Date: 2010-01-19
ISBN-10 / ASIN: 1441917632
ISBN-13 / EAN: 9781441917638

Product Description:

This book covers recent advances for quantitative researchers with practical examples from social sciences. The twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software.
McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime.
Koenker: Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition.
Gelman: R graphics in the context of voter participation in US elections.
Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment.
Markus and Gu: New R tools for exploratory data analysis including bubble plots.
Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts; relevant to anyone with an investment portfolio.
Foster and Kecojevic: Extends the usual analysis of covariance (ANCOVA) illustrated by growth charts for Saudi children.
Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment.
Haupt, Schnurbus, and Tschernig: Consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation.
Rindskopf: R methods to fit a multinomial based multivariate analysis of variance (ANOVA) with examples from psychology, sociology, political science, and medicine. Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site.
Numatsi and Rengifo: Explain persistent discrete jumps in financial series subject to misspecification.

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2009-12-31 01:16:26

Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
A Brief Review of Each Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1 Econometric Computing with “R” . . . . . . . . . . . . . . . . . . . . . . . 1
B. D. McCullough
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Economics Profession Needs Econometric Computing . . 3
1.2.1 Most Users Do Not Know Econometric Computing . 3
1.2.2 Some Developers Do Not Know Econometric
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 Some Textbook Authors Do Not Know
Econometric Computing . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Econometric Computing Is Important . . . . . . . . . . . . . . . . . . . . 6
1.4 “R” Is the Best Language for Teaching Econometric
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 The Longley Data and Econometric Computing . . . . . . . . . . . 10
1.6 Beaton, Rubin and Barone Revisit Longley . . . . . . . . . . . . . . . 12
1.7 An Example: Donohue/Levitt’s Abortion Paper . . . . . . . . . . . 14
1.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Additive Models for Quantile Regression: An Analysis of
Risk Factors for Malnutrition in India . . . . . . . . . . . . . . . . . . . . 23
Roger Koenker
2.1 Additive Models for Quantile Regression . . . . . . . . . . . . . . . . . 24
2.2 A Model of Childhood Malnutrition in India . . . . . . . . . . . . . . 25
2.2.1 l-Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.2 Confidence Bands and Post-Selection Inference . . . . 28
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Toward Better R Defaults for Graphics: Example of
Voter Turnouts in U.S. Elections . . . . . . . . . . . . . . . . . . . . . . . . . 35
Andrew Gelman
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 Superior Estimation and Inference Avoiding
Heteroscedasticity and Flawed Pivots: R-example
of Inflation Unemployment Trade-Off . . . . . . . . . . . . . . . . . . . . . 39
H. D. Vinod
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Heteroscedasticity Efficient (HE) Estimation . . . . . . . . . . . . . . 42
4.3 A Limited Monte Carlo Simulation
of Efficiency of HE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 An Example of Heteroscedasticity Correction . . . . . . . . . . . . . 51
4.5 Superior Inference of Deep Parameters
Beyond Efficient Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.6 Summary and Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5 Bubble Plots as a Model-Free Graphical Tool for
Continuous Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Keith A. Markus and Wen Gu
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 General Principles Bearing on Three-Way Graphs . . . . . . . . . 66
5.3 Graphical Options Ruled Out a Priori . . . . . . . . . . . . . . . . . . . 68
5.4 Plausible Graphical Alternatives . . . . . . . . . . . . . . . . . . . . . . . . 71
5.5 The bp3way() Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.5.1 Use and Options of bp3way() Function. . . . . . . . . . . . 75
5.5.2 Six Key Parameters for Controlling the Graph . . . . . 75
5.5.3 Additional Parameters Controlling the Data Plotted 76
5.5.4 Parameters Controlling the Plotted Bubbles . . . . . . . 76
5.5.5 Parameters Controlling the Grid . . . . . . . . . . . . . . . . . 77
5.5.6 The tacit Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5.7 The bp.data() Function . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.6 An Empirical Study of Three Graphical Methods . . . . . . . . . . 78
5.6.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Appendixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6 Combinatorial Fusion for Improving Portfolio
Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
H. D. Vinod, D. F. Hsu and Y. Tian
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.2 Combinatorial Fusion Analysis
for Portfolios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3 An Illustrative Example as an Experiment . . . . . . . . . . . . . . . . 100
6.3.1 Description of the Data Set . . . . . . . . . . . . . . . . . . . . . 100
6.3.2 Description of the Steps in Our R Algorithm . . . . . . 102
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7 Reference Growth Charts for Saudi Arabian Children
and Adolescents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
P. J. Foster and T. Kecojevi´c
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.2 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.3 LMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4 Smoothing and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
7.5 Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.6 Comparisons Using ANCOVA . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7.6.1 Comparing Geographical Regions . . . . . . . . . . . . . . . . 122
7.6.2 Comparing Males and Females . . . . . . . . . . . . . . . . . . . 125
7.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

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8 Causal Mediation Analysis Using R. . . . . . . . . . . . . . . . . . . . . . . 129
K. Imai, L. Keele, D. Tingley, and T. Yamamoto
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8.1.1 Installation and Updating . . . . . . . . . . . . . . . . . . . . . . . 130
8.2 The Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8.2.2 Estimation of the Causal Mediation Effects . . . . . . . . 132
8.2.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
8.2.4 Current Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8.3.1 Estimation of Causal Mediation Effects . . . . . . . . . . . 138
8.3.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
8.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
8.5 Notes and Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
9 Statistical Validation of Functional Form in Multiple
Regression Using R. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Harry Haupt, Joachim Schnurbus, and Rolf Tschernig
9.1 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
9.2 Nonparametric Methods for Model Validation . . . . . . . . . . . . . 157
9.3 Model Visualization and Validation Using relax . . . . . . . . . . 159
9.4 Beauty and the Labor Market Revisited . . . . . . . . . . . . . . . . . . 161
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
10 Fitting Multinomial Models in R: A Program Based on
Bock’s Multinomial Response Relation Model . . . . . . . . . . . . 167
David Rindskopf
10.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
10.2 Program Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
10.3 How to Use the mqual Function . . . . . . . . . . . . . . . . . . . . . . . . . 169
10.4 Example 1: Test of Independence . . . . . . . . . . . . . . . . . . . . . . . . 170
10.4.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
10.4.2 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
10.5 Example 2: Effect of Aspirin on Myocardial Infarction (MI) . 171
10.5.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
10.5.2 Output from Saturated Model . . . . . . . . . . . . . . . . . . . 171
10.6 Example 3: Race × Gender × Party Affiliation . . . . . . . . . . . . 172
10.6.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
10.6.2 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
10.7 Nonstandard Loglinear Models . . . . . . . . . . . . . . . . . . . . . . . . . . 174
10.8 Technical Details of Estimation Procedure . . . . . . . . . . . . . . . . 174
10.9 Troubleshooting and Usage Suggestions . . . . . . . . . . . . . . . . . . 176
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
11 A Bayesian Analysis of Leukemia Incidence Surrounding
an Inactive Hazardous Waste Site . . . . . . . . . . . . . . . . . . . . . . . . 179
Ronald C. Neath
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
11.2 Data Summaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
11.3 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
11.4 Prior Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
11.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
11.5.1 Estimated Posteriors . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
11.5.2 The Location-Risk Function . . . . . . . . . . . . . . . . . . . . . 187
11.5.3 A Simplified Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
11.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
12 Stochastic Volatility Model with Jumps in Returns and
Volatility: An R-Package Implementation . . . . . . . . . . . . . . . . . 191
Adjoa Numatsi and Erick W. Rengifo
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
12.2 The Stochastic Volatility Model with Jumps in Returns
and Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
12.3 Empirical Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
12.3.1 The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
12.3.2 The Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . 194
12.3.3 The R Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
12.3.4 The Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
12.4 Conclusion and Future Venues of Research . . . . . . . . . . . . . . . 200
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203