Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models

This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise. We study the kernel estimator of the latter function when th...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-02, Vol.10 (4), p.624
Main Authors: Ngatchou-Wandji, Joseph, Ltaifa, Marwa, Njamen Njomen, Didier Alain, Shen, Jia
Format: Article
Language:English
Subjects:
Autoregressive models
Bandwidths
Density
Food science
kernel estimation
Mathematical functions
Methods
Noise
nonlinear heteroscedastic model
Nonparametric statistics
Normality
Parameter estimation
Sample variance
Time series
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Summary:This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise. We study the kernel estimator of the latter function when the former are either parametric or nonparametric. The consistency, bias and asymptotic normality of the estimator are investigated. Confidence bound curves are given. A simulation experiment is performed to evaluate the performance of the results.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10040624