Quantile Regression

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.

• Preface ixAcknowledgments xiIntroduction xiiNomenclature xv1 A visual introduction to quantile regression 1Introduction 11.1 The essential toolkit 11.2 The simplest QR model: The case of the dummy regressor 81.3 A slightly more complex QR model: The case of a nominal regressor 131.4 A typical QR model: The case of a quantitative regressor 151.5 Summary of key points 20References 212 Quantile regression: Understanding how and why 22Introduction 222.1 How and why quantile regression works 222.2 A set of illustrative artificial data 332.3 How and why to work with QR 382.4 Summary of key points 60References 623 Estimated coefficients and inference 64Introduction 643.1 Empirical distribution of the quantile regression estimator 643.2 Inference in QR, the i.i.d. case 763.3 Wald, Lagrange multiplier, and likelihood ratio tests 843.4 Summary of key points 92References 934 Additional tools for the interpretation and evaluation of the quantile regression model 94Introduction 944.1 Data pre-processing 954.2 Response conditional density estimations 1074.3 Validation of the model 1174.4 Summary of key points 128References 1285 Models with dependent and with non-identically distributed data 131Introduction 1315.1 A closer look at the scale parameter, the independent and identically distributed case 1315.2 The non-identically distributed case 1375.3 The dependent data model 1525.4 Summary of key points 158References 158Appendix 5.A Heteroskedasticity tests and weighted quantile regression, Stata and R codes 1595.A.1 Koenker and Basset test for heteroskedasticity comparing two quantile regressions 1595.A.2 Koenker and Basset test for heteroskedasticity comparing all quantile regressions 1595.A.3 Quick tests for heteroskedasticity comparing quantile regressions 1605.A.4 Compute the individual role of each explanatory variable to the dependent variable 1615.A.5 R-codes for the Koenker and Basset test for heteroskedasticity 161Appendix 5.B Dependent data 1626 Additional models 163Introduction 1636.1 Nonparametric quantile regression 1636.2 Nonlinear quantile regression 1726.3 Censored quantile regression 1756.4 Quantile regression with longitudinal data 1836.5 Group effects through quantile regression 1876.6 Binary quantile regression 1956.7 Summary of key points 197References 197Appendix A Quantile regression and surroundings using R 201Introduction 201A.1 Loading data 202A.2 Exploring data 205A.3 Modeling data 211A.4 Exporting figures and tables 217References 218Appendix B Quantile regression and surroundings using SAS 220Introduction 220B.1 Loading data 221B.2 Exploring data 223B.3 Modeling data 229B.4 Exporting figures and tables 239References 241Appendix C Quantile regression and surroundings using Stata 242Introduction 242C.1 Loading data 243C.2 Exploring data 245C.3 Modeling data 249C.4 Exporting figures and tables 255References 256Index 257