Robust and efficient estimation with weighted composite quantile regression

Xuejun Jiang, Jingzhi Li, Tian Xia and Wanfeng Yan

Physica A: Statistical Mechanics and its Applications, 2016, vol. 457, issue C, 413-423

Abstract: In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student’s t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

Keywords: Weighted CQR; Oracle MLE; Extended interior algorithm; Double threshold ARCH models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:457:y:2016:i:c:p:413-423

DOI: 10.1016/j.physa.2016.03.056

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