Weighted empirical likelihood inferences for a class of varying coefficient ARCH-M models. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Zhao, Peixin; Yang, Yiping; Zhou, Xiaoshuang
• Is Part Of: Journal of nonparametric statistics. Volume 33:Issue 1(2021)
• Page Start: 1
• Page End: 20
• Publication Date: 2021-01-02
• Subjects: Varying coefficient model -- ARCH-M model -- weighted empirical likelihood -- confidence interval; 62G05 -- 62G20 -- 62G30; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2021.1898608
• Languages: English
• ISSNs: 1048-5252

Abstract

Abstract : In this paper, we consider the empirical likelihood inferences for a class of varying coefficient ARCH-M models, which is an extended version of parametric ARCH-M models. By constructing a weighted auxiliary random vector, we propose a weighted empirical likelihood method for estimating the functional-coefficients. Under some regularity conditions, the constructed empirical log-likelihood ratio is shown to be asymptotically χ 2, and then the pointwise confidence interval for functional-coefficient is constructed. Some simulation studies are carried out to compare finite sample performances of the proposed empirical likelihood estimation method with some existing estimation methods under various model settings. A real data analysis is also undertaken to illustrate practical implementation and performance of the proposed estimation procedure.