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Informationen zum Autor 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. Klappentext A guide to the implementation and interpretation of Quantile Regression modelsThis 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. Zusammenfassung 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. Inhaltsverzeichnis 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 Koenke...
