Cristina Davino, Marilena Furno, Domenico Vistocco

Quantile Regression (eBook, PDF)

Theory and Applications

• Format: PDF

Produktbeschreibung

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: * Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. * Delivers a balance between methodolgy and application * Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. * Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 288
• Erscheinungstermin: 18. Oktober 2013
• Englisch
• ISBN-13: 9781118753194
• Artikelnr.: 39906565

Autorenporträt

Cristina Davino is the author of Quantile Regression: Theory and Applications, published by Wiley. Marilena Furno, Department of Agriculture, University of Naples Federico II, Italy. Domenico Vistocco, Department of Economics and Law, University of Cassino, Italy.

Inhaltsangabe

Preface ix Acknowledgments xi Introduction xii Nomenclature xv 1 A visual
introduction to quantile regression 1 Introduction 1 1.1 The essential
toolkit 1 1.2 The simplest QR model: The case of the dummy regressor 8 1.3
A slightly more complex QR model: The case of a nominal regressor 13 1.4 A
typical QR model: The case of a quantitative regressor 15 1.5 Summary of
key points 20 References 21 2 Quantile regression: Understanding how and
why 22 Introduction 22 2.1 How and why quantile regression works 22 2.2 A
set of illustrative artificial data 33 2.3 How and why to work with QR 38
2.4 Summary of key points 60 References 62 3 Estimated coefficients and
inference 64 Introduction 64 3.1 Empirical distribution of the quantile
regression estimator 64 3.2 Inference in QR, the i.i.d. case 76 3.3 Wald,
Lagrange multiplier, and likelihood ratio tests 84 3.4 Summary of key
points 92 References 93 4 Additional tools for the interpretation and
evaluation of the quantile regression model 94 Introduction 94 4.1 Data
pre-processing 95 4.2 Response conditional density estimations 107 4.3
Validation of the model 117 4.4 Summary of key points 128 References 128 5
Models with dependent and with non-identically distributed data 131
Introduction 131 5.1 A closer look at the scale parameter, the independent
and identically distributed case 131 5.2 The non-identically distributed
case 137 5.3 The dependent data model 152 5.4 Summary of key points 158
References 158 Appendix 5.A Heteroskedasticity tests and weighted quantile
regression, Stata and R codes 159 5.A.1 Koenker and Basset test for
heteroskedasticity comparing two quantile regressions 159 5.A.2 Koenker and
Basset test for heteroskedasticity comparing all quantile regressions 159
5.A.3 Quick tests for heteroskedasticity comparing quantile regressions 160
5.A.4 Compute the individual role of each explanatory variable to the
dependent variable 161 5.A.5 R-codes for the Koenker and Basset test for
heteroskedasticity 161 Appendix 5.B Dependent data 162 6 Additional models
163 Introduction 163 6.1 Nonparametric quantile regression 163 6.2
Nonlinear quantile regression 172 6.3 Censored quantile regression 175 6.4
Quantile regression with longitudinal data 183 6.5 Group effects through
quantile regression 187 6.6 Binary quantile regression 195 6.7 Summary of
key points 197 References 197 Appendix A Quantile regression and
surroundings using R 201 Introduction 201 A.1 Loading data 202 A.2
Exploring data 205 A.3 Modeling data 211 A.4 Exporting figures and tables
217 References 218 Appendix B Quantile regression and surroundings using
SAS 220 Introduction 220 B.1 Loading data 221 B.2 Exploring data 223 B.3
Modeling data 229 B.4 Exporting figures and tables 239 References 241
Appendix C Quantile regression and surroundings using Stata 242
Introduction 242 C.1 Loading data 243 C.2 Exploring data 245 C.3 Modeling
data 249 C.4 Exporting figures and tables 255 References 256 Index 257