A Time-Varying Mixture Integer-Valued Threshold Autoregressive Process Driven by Explanatory Variables

Bibliographic Details
Title:	A Time-Varying Mixture Integer-Valued Threshold Autoregressive Process Driven by Explanatory Variables
Authors:	Danshu Sheng, Dehui Wang, Jie Zhang, Xinyang Wang, Yiran Zhai
Source:	Entropy, Vol 26, Iss 2, p 140 (2024)
Publisher Information:	MDPI AG, 2024.
Publication Year:	2024
Collection:	LCC:Science LCC:Astrophysics LCC:Physics
Subject Terms:	threshold integer-valued autoregressive models, mixture thinning operator, parameter estimation, Wald test, explanatory variables, Science, Astrophysics, QB460-466, Physics, QC1-999
More Details:	In this paper, a time-varying first-order mixture integer-valued threshold autoregressive process driven by explanatory variables is introduced. The basic probabilistic and statistical properties of this model are studied in depth. We proceed to derive estimators using the conditional least squares (CLS) and conditional maximum likelihood (CML) methods, while also establishing the asymptotic properties of the CLS estimator. Furthermore, we employed the CLS and CML score functions to infer the threshold parameter. Additionally, three test statistics to detect the existence of the piecewise structure and explanatory variables were utilized. To support our findings, we conducted simulation studies and applied our model to two applications concerning the daily stock trading volumes of VOW.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	1099-4300
Relation:	https://www.mdpi.com/1099-4300/26/2/140; https://doaj.org/toc/1099-4300
DOI:	10.3390/e26020140
Access URL:	https://doaj.org/article/b7111ab2e094422a869d267979e53ce4
Accession Number:	edsdoj.b7111ab2e094422a869d267979e53ce4
Database:	Directory of Open Access Journals
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More Details
ISSN:	10994300
DOI:	10.3390/e26020140
Published in:	Entropy
Language:	English