Sheep in Wolf’s clothing: Using the least squares criterion for quantile estimation

This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The quantile coupling allows one to apply the standard Gaussian-based estimation and inference to the transformed data set. The resulting estimator is asymptotically normal with a parametric convergence rate. This method is faster than the conventional check function approach, when handling a sizable data set.

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Year of publication:	2014
Authors:	Chen, Heng
Published in:	Economics Letters. - Elsevier, ISSN 0165-1765. - Vol. 125.2014, 3, p. 426-431
Publisher:	Elsevier
Subject:	Quantile coupling \| Quantile model \| Least squares estimation

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Type of publication:	Article
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods ; C21 - Cross-Sectional Models; Spatial Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011116222