Roger KoenkerCambridge University PressEdition: Illustrated, 6/13/2014EAN 9780521608275, ISBN10: 0521608279Paperback, 100 pages, 22.9 x 15.2 x 2.3 cmLanguage: EnglishOriginally published in EnglishQuantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.Part I. Introduction1. Means and ends2. The first regressionan historical prelude3. Quantiles, ranks, and optimization4. Preview of quantile regression5. Three examples6. ConclusionPart II. Fundamentals of Quantile Regression7. Quantile treatment effects8. How does quantile regression work?9. Robustness10. Interpreting quantile regression models11. Cautionquantile crossing12. A random coefficient interpretation13. Inequality measures and their decomposition14. Expectiles and other variations15. Interpreting misspecified quantile regressions16. ProblemsPart III. Inference for Quantile Regression17. The finite sample distribution of regression quantiles18. A heuristic introduction to quantile regression asymptotics19. Wald tests20. Estimation of asymptotic covariance matrices21. Rank based Inference for quantile regression22. Quantile likelihood ratio tests23. Inference on the quantile regression process24. Tests of the location/acale hypothesis25. Resampling methods and the bootstrap26. Monte-Carlo comparison of methods27. ProblemsPart IV. Asymptotic Theory of Quantile Regression28. Consistency29. Rates of convergence30. Bahadur representation31. Nonlinear quantile regression32. The quantile regression rankscore process33. Quantile regression asymptotics under dependent conditions34. Extremal quantile regression35. The method of quantiles36. Model selection, penalties, and large-p asymptotics37. Asymptotics for inference38. Resampling schemes and the bootstrap39. Asymptotics for the quantile regression process40. ProblemsPart V. L-Statistics and Weighted Quantile Regression41. L-Statistics for the linear model42. Kernel smoothing for quantile regression43. Weighted quantile regression44 Quantile regression for location-scale models45. Weighted sums of p-functions46. ProblemsPart VI. Computational Aspects of Quantile Regression47. Introduction to linear programming48. Simplex methods for quantile regression49. Parametric programming for quantile regression50 Interior point methods for canonical LPs51. Preprocessing for quantile regression52. Nonlinear quantile regression53. Inequality constraints54. Weighted sums of p-functions55. Sparsity56. Conclusion57. ProblemsPart VII. Nonparametric Quantile Regression58. Locally polynomial quantile regression59. Penalty methods for univariate smoothing60. Penalty methods for bivariate Smoothing61. Additive models and the Role of sparsityPart VIII. Twilight Zone of Quantile Regression62. Quantile regression for survival data63. Discrete Response models64. Quantile autoregression65. Copula functions and nonlinear quantile regression66. High breakdown alternatives to quantile regression67. Multivariate quantiles68. Penalty methods for longitudinal data69. Causal effects and structural models70. Choquet utility, risk and pessimistic portfoliosPart IX. ConclusionA. Quantile regression in Ra vignetteA.1. IntroductionA.2. What is a vignette?A.3. Getting startedA.4. Object orientationA.5. Formal InferenceA.6. More on testingA.7. Inference on the quantile regression processA.8. Nonlinear quantile regressionA.9. Nonparametric quantile regressionA.10. ConclusionB. Asymptotic critical values.
