Advances in Social Science Research Using R

by Vinod, Hrishikesh D.

• ISBN13: 9781441917638
• ISBN10: 1441917632
• Edition: 1st
• Format: Paperback
• Copyright: 2010-01-22
• Publisher: Springer Nature

Summary

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.

Table of Contents

Preface	p. vii
A Brief Review of Each Chapter	p. viii
References	p. xi
Acknowledgments	p. xiii
Econometric Computing with "R"	p. 1
Introduction	p. 1
The Economics Profession Needs Econometric Computing	p. 3
Most Users Do Not Know Econometric Computing	p. 3
Some Developers Do Not Know Econometric Computing	p. 4
Some Textbook Authors Do Not Know Econometric Computing	p. 4
Econometric Computing Is Important	p. 6
"R" Is the Best Language for Teaching Econometric Computing	p. 8
The Longley Data and Econometric Computing	p. 10
Beaton, Rubin and Barone Revisit Longley	p. 12
An Example: Donohue/Levitt's Abortion Paper	p. 14
Conclusions	p. 19
References	p. 19
Additive Models for Quantile Regression: An Analysis of Risk Factors for Malnutrition in India	p. 23
Additive Models for Quantile Regression	p. 24
A Model of Childhood Malnutrition in India	p. 25
A-Selection	p. 26
Confidence Bands and Post-Selection Inference	p. 28
References	p. 32
Toward Better R Defaults for Graphics: Example of Voter Turnouts in U.S. Elections	p. 35
References	p. 38
Superior Estimation and Inference Avoiding Heteroscedasticity and Flawed Pivots: R-example of Inflation Unemployment Trade-Off	p. 39
Introduction	p. 40
Heteroscedasticity Efficient (HE) Estimation	p. 42
A Limited Monte Carlo Simulation of Efficiency of HE	p. 49
An Example of Heteroscedasticity Correction	p. 51
Superior Inference of Deep Parameters Beyond Efficient Estimation	p. 57
Summary and Final Remarks	p. 58
Appendix	p. 59
References	p. 62
Bubble Plots as a Model-Free Graphical Tool for Continuous Variables	p. 65
Introduction	p. 65
General Principles Bearing on Three-Way Graphs	p. 66
Graphical Options Ruled Out a Priori	p. 68
Plausible Graphical Alternatives	p. 71
The bp3way() Function	p. 74
Use and Options of bp3way() Function	p. 75
Six Key Parameters for Controlling the Graph	p. 75
Additional Parameters Controlling the Data Plotted	p. 76
Parameters Controlling the Plotted Bubbles	p. 76
Parameters Controlling the Grid	p. 77
The tacit Parameter	p. 77
The bp.data() Function	p. 77
An Empirical Study of Three Graphical Methods	p. 78
Method	p. 78
Results	p. 80
Discussion	p. 89
Appendixes	p. 91
References	p. 93
Combinatorial Fusion for Improving Portfolio Performance	p. 95
Introduction	p. 96
Combinatorial Fusion Analysis for Portfolios	p. 97
An Illustrative Example as an Experiment	p. 100
Description of the Data Set	p. 100
Description of the Steps in Our R Algorithm	p. 102
References	p. 104
Reference Growth Charts for Saudi Arabian Children and Adolescents	p. 107
Introduction	p. 108
Outliers	p. 108
LMS	p. 113
Smoothing and Evaluation	p. 117
Averaging	p. 118
Comparisons Using ANCOVA	p. 122
Comparing Geographical Regions	p. 122
Comparing Males and Females	p. 125
Discussion	p. 126
References	p. 128
Causal Mediation Analysis Using R	p. 129
Introduction	p. 130
Installation and Updating	p. 130
The Software	p. 131
Overview	p. 131
Estimation of the Causal Mediation Effects	p. 132
Sensitivity Analysis	p. 134
Current Limitations	p. 136
Examples	p. 138
Estimation of Causal Mediation Effects	p. 138
Sensitivity Analysis	p. 147
Concluding Remarks	p. 153
Notes and Acknowledgment	p. 153
References	p. 153
Statistical Validation of Functional Form in Multiple Regression Using R	p. 155
Model Validation	p. 155
No a parametric Methods for Model Validation	p. 157
Model Visualization and Validation Using relax	p. 159
Beauty and the Labor Market Revisited	p. 161
References	p. 166
Fitting Multinomial Models in R: A Program Based on Bock's Multinomial Response Relation Model	p. 167
Model	p. 167
Program Code	p. 169
How to Use the mqual Function	p. 169
Example 1: Test of Independence	p. 170
Input	p. 170
Output	p. 170
Example 2: Effect of Aspirin on Myocardial Infarction (MI)	p. 171
Input	p. 171
Output from Saturated Model	p. 171
Race x Gender x Party Affiliation	p. 172
Input	p. 172
Output	p. 173
Nonstandard Loglinear Models	p. 174
Technical Details of Estimation Procedure	p. 174
Troubleshooting and Usage Suggestions	p. 176
References	p. 177
A Bayesian Analysis of Leukemia Incidence Surrounding an Inactive Hazardous Waste Site	p. 179
Introduction	p. 179
Data Summaries	p. 180
The Model	p. 180
Prior Distributions	p. 183
Analysis	p. 184
Estimated Posteriors	p. 185
The Location-Risk Function	p. 187
A Simplified Model	p. 188
Discussion	p. 189
References	p. 190
Stochastic Volatility Model with Jumps in Returns and Volatility: An R-Package Implementation	p. 191
Introduction	p. 191
The Stochastic Volatility Model with Jumps in Returns and Volatility	p. 193
Empirical Implementation	p. 194
The Data	p. 194
The Estimation Method	p. 194
The R Program	p. 197
The Results	p. 197
Conclusion and Future Venues of Research	p. 200
References	p. 200
Index	p. 203
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