Quantile Regression : Econometric Society Monographs - Roger Koenker

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Quantile Regression

By: Roger Koenker

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Hardcover | 5 May 2005

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Hardcover
368 Pages

Dimensions(cm)
23.5 x 16.1 x 2.9

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Quantile 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.

Industry Reviews

'... well written and easy to read, with useful illustrations of important aspects of quantile regression. It is obvious that the author knows the subject inside out, giving an up-to-date, exhaustive account of the subject. ... The book is a valuable contribution to the statistical literature, and a must have for every statistician or econometrician interested in quantile regression methods.' Journal of the Royal Statistical Society 'It is well written and easy to read, with useful ilustrations of important aspects of quantile regression ... a valuable contribution to the statistical literature and is essential for every statistician or econometrician who is interested in quantile regression methods.' Andreas Karlsson, Uppsala University

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Introduction
Means and ends
The first regression: an historical prelude
Quantiles, ranks, and optimization
Preview of quantile regression
Three examples
Conclusion
Fundamentals of Quantile Regression
Quantile treatment effects
How does quantile regression work?
Robustness
Interpreting quantile regression models
Caution: quantile crossing
A random coefficient interpretation
Inequality measures and their decomposition
Expectiles and other variations
Interpreting misspecified quantile regressions
Problems
Inference for Quantile Regression
The finite sample distribution of regression quantiles
A heuristic introduction to quantile regression asymptotics
Wald tests
Estimation of asymptotic covariance matrices
Rank based Inference for quantile regression
Quantile likelihood ratio tests
Inference on the quantile regression process
Tests of the location/acale hypothesis
Resampling methods and the bootstrap
Monte-Carlo comparison of methods
Problems
Asymptotic Theory of Quantile Regression
Consistency
Rates of convergence
Bahadur representation
Nonlinear quantile regression
The quantile regression rankscore process
Quantile regression asymptotics under dependent conditions
Extremal quantile regression
The method of quantiles
Model selection, penalties, and large-p asymptotics
Asymptotics for inference
Resampling schemes and the bootstrap
Asymptotics for the quantile regression process
Problems
L-Statistics and Weighted Quantile Regression
L-Statistics for the linear model
Kernel smoothing for quantile regression
Weighted quantile regression
Quantile regression for location-scale models
Weighted sums of p-functions
Problems
Computational Aspects of Quantile Regression
Introduction to linear programming
Simplex methods for quantile regression
Parametric programming for quantile regression
Interior point methods for canonical LPs
Preprocessing for quantile regression
Nonlinear quantile regression
Inequality constraints
Weighted sums of p-functions
Sparsity
Conclusion
Problems
Nonparametric Quantile Regression
Locally polynomial quantile regression
Penalty methods for univariate smoothing
Penalty methods for bivariate Smoothing
Additive models and the Role of sparsity
Twilight Zone of Quantile Regression
Quantile regression for survival data
Discrete Response models
Quantile autoregression
Copula functions and nonlinear quantile regression
High breakdown alternatives to quantile regression
Multivariate quantiles
Penalty methods for longitudinal data
Causal effects and structural models
Choquet utility, risk and pessimistic portfolios
Conclusion
Quantile regression in R: a vignette
Introduction
What is a vignette?
Getting started
Object orientation
Formal Inference
More on testing
Inference on the quantile regression process
Nonlinear quantile regression
Nonparametric quantile regression
Conclusion
Asymptotic critical values
Table of Contents provided by Publisher. All Rights Reserved.

Quantile Regression

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

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