Abstract Details
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Activity Number:
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441
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 AM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #310427 |
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Title:
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Quantile Regression with Heteroskedasticity and Asymmetry
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Author(s):
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David J. Mauler*+ and James B. McDonald
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Companies:
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Brigham Young University and Brigham Young University
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Keywords:
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Quantile Regression ;
Heteroskedasticity ;
Partially Adaptive Estimation ;
Asymmetry
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Abstract:
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This paper presents a quantile regression model which can accommodate heteroskedasticity and skewed dependent variables. Using a partially adaptive estimation framework, we simultaneously estimate the regression and distributional parameters. This approach lends itself to testing for the presence of heteroskedasticity and asymmetry. Monte Carlo simulations and an application are used to explore the properties of this approach relative to traditional quantile regression methods
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