Weighted empirical likelihood inferences for a class of varying coefficient ARCH-M models

Peixin Zhao, Yiping Yang and Xiaoshuang Zhou

Journal of Nonparametric Statistics, 2021, vol. 33, issue 1, 1-20

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 $ \chi ^2 $ χ2, 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.

Date: 2021
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DOI: 10.1080/10485252.2021.1898608

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