CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2014, 30(6) 631-650 DOI:      ISSN: 1001-4268 CN: 31-1256

Current Issue | Archive | Search                                                            [Print]   [Close]
article
Information and Service
This Article
Supporting info
PDF(1611KB)
[HTML]
Reference
Service and feedback
Email this article to a colleague
Add to Bookshelf
Add to Citation Manager
Cite This Article
Email Alert
Keywords
Authors
PubMed

Local Weighted Composite Quantile Estimating for Varying Coefficient Models

Xie Qichang, Lv Xiumei

School of Economics, Shandong Institute of Business and Technology; School of Finance, Chongqing Technology and Business University

Abstract��

A generalization of classical linear models is varying coefficient
models, which offer a flexible approach to modeling nonlinearity between covariates. A
method of local weighted composite quantile regression is suggested to estimate the
coefficient functions. The local Bahadur representation of the local estimator is derived
and the asymptotic normality of the resulting estimator is established. Comparing to the
local least squares estimator, the asymptotic relative efficiency is examined for the local
weighted composite quantile estimator. Both theoretical analysis and numerical simulations
reveal that the local weighted composite quantile estimator can obtain more efficient than
the local least squares estimator for various non-normal errors. In the normal error case,
the local weighted composite quantile estimator is almost as efficient as the local least
squares estimator. Monte Carlo results are consistent with our theoretical findings. An
empirical application demonstrates the potential of the proposed method.

Keywords��
Received  Revised  Online:  
DOI:
Fund:
Corresponding Authors: Xie Qichang
Email:
About author:

References��
Similar articles

Copyright by CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST