Quantile Regression |
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Comment from the Stata technical groupQuantile Regression, by Lingxin Hao and Daniel Q. Naiman, provides an excellent introduction to quantile-regression methods. The intuitive explanations and many examples make this book easy to read and understand. An appendix provides Stata commands to replicate the examples using the datasets available at http://www.ams.jhu.edu/~hao/QRbook_data_codes/. After showing the advantages that quantile regression has over least squares, the authors discuss the estimation technique, the statistical inference, and how to interpret the results. The example-based approach is exceptionally clear and avoids swamping the reader in technical details. The final section of the monograph applies the techniques to changes in U.S. income equality between 1991 and 2001. This application illustrates both how to use the methods and how to interpret the results. |
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Table of contentsView table of contents >> Series Editor’s Introduction
Acknowledgments
1. Introduction
2. Quantiles and Quantile Functions
CDFs, Quantiles, and Quantile Functions
Sampling Distribution of a Sample Quantile Quantile-Based Measures of Location and Shape Quantile as a Solution to a Certain Minimization Problem Properties of Quantiles Summary Note Chapter 2 Appendix: A Proof: Median and Quantiles as Solutions to a Minimization Problem 3. Quantile-Regression Model and Estimation
Linear-Regression Modeling and Its Shortcomings
Conditional-Median and Quantile-Regression Models QR Estimation Transformation and Equivariance Summary Notes 4. Quantile-Regression Inference
Standard Errors and Confidence Intervals for the LRM
Standard Errors and Confidence Intervals for the QRM The Bootstrap Method for the QRM Goodness of Fit of the QRM Summary Note 5. Interpretation of Quantile-Regression Estimates
Reference and Comparison
Conditional Means Versus Conditional Medians Interpretation of Other Individual Conditional Quantiles Tests for Equivalence of Coefficients Across Quantiles Using the QRM Results to Interpret Shape Shifts Summary Notes 6. Interpretation of Monotone-Transformed QRM
Location Shifts on the Log Scale
From Log Scale Back to Raw Scale Graphical View of Log-Scale Coefficients Shape-Shift Measures from Log-Scale Fits Summary Notes 7. Application to Income Inequality in 1991 and 2001
Observed Income Disparity
Descriptive Statistics Notes on Survey Income Data Goodness of Fit Conditional-Mean Versus Conditional-Median Regression Graphical View of QRM Estimates from Income and Log-Income Equations Quantile Regressions at Noncentral Positions: Effects in Absolute Terms Assessing a Covariate’s Effect on Location and Shape Shifts Summary Appendix: Stata Codes
References
Index
About the Authors
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