Chapter 1 Introduction
1.1 Overview
1.2 Quantile Regression and Its Applications
References
Chapter 2 Robust Statistics and Robust Regressions
2.1 Introduction to Classical and Robust Approaches to Statistics
2.2 Least Squares Linear Regression
2.3 Robust Regression
2.3.1 Least Absolute Values Regression
2.3.2 M-estimator
2.4 Quantile Regression
2.4.1 Quantile Regression Model
2.4.2 The Finite-sample Distribution of Regression Quantiles
2.4.3 Quantile Regression Asymptotics
2.4.4 Wald Tests
2.4.5 Estimation of Asymptotic Covariance Matrix
2.4.6 Quantile Likelihood Ratio Tests
References
Chapter 3 Robust Estimates of Covariance
3.1 Conventional Measure of Covariance
3.2 Robust Measures of Covariance
3.2.1 Median Absolute Deviation About the Median (MAD)
3.2.2 Gnanadesikan and Ketenring Robust Measures of Covariance
3.2.3 M-estimates
3.2.4 Minimum Volume Ellipsoid Estimate (MVE)
3.2.5 S-estimates
3.2.6 Minimum Covariance Determinant Estimate (MCD)
3.3 An Alternative Robust Measure of Covariance
3.4 Monte Carlo Simulations
3.5 Empirical Application
3.5.1 Empirical Comparison of Robust Estimates
3.5.2 Portfolio Performances of Robust Covariances
3.6 Conclusion
3.7 Appendix: Derivation of Conventional Covariance with Outlier(s)
References
Chapter 4 Quantile Regression Serial Correlation Tests
4.1 Spurious Autocorrelation in Quantile Models
4.1.1 Standard LM Test for Linear Model with AR(p) Errors
4.1.2 Theoretical Explanation to the Occurance of Spurious Autocorrelation
4.2 Correctly-sized Tests
4.2.1 QF test
4.2.2 The QR-LM Test
4.3 Monte-Carlo Simulations
4.4 An Empirical Example
4.5 Conclusion
4.6 Appendix
References
Chapter 5 Growth Empirics Based on IV Panel Quantile Regression
5.1 Economic Growth Convergence
5.2 Quantile Regression for Panel Data Model with Fixed Effects
