Partially adaptive quantile estimators

This paper contrasts two approaches to estimating quantile regression models: traditional semi-parametric methods and partially adaptive estimators using flexible probability density functions (pdfs). While more general pdfs could have been used, the skewed Laplace was selected for pedagogical purpo...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2017-06, Vol.46 (11), p.5327-5341
Main Authors: Mauler, David J., McDonald, James B., Tatham, Logan C.
Format: Article
Language:English
Subjects:
Computer simulation
Education
Estimating
Estimators
Heteroskedasticity
Monte Carlo
Probability density functions
Quantiles
Regression
Regression analysis
Semi-parametric
Skewed Laplace
Skewness
Statistics
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Summary:This paper contrasts two approaches to estimating quantile regression models: traditional semi-parametric methods and partially adaptive estimators using flexible probability density functions (pdfs). While more general pdfs could have been used, the skewed Laplace was selected for pedagogical purposes. Monte Carlo simulations are used to compare the behavior of the semi-parametric and partially adaptive quantile estimators in the presence of possibly skewed and heteroskedastic data. Both approaches accommodate skewness and heteroskedasticity which are consistent with linear quantiles; however, the partially adaptive estimator considered allows for non linear quantiles and also provides simple tests for symmetry and heteroskedasticity. The methods are applied to the problem of estimating conditional quantile functions for wages corresponding to different levels of education.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2015.1096391