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Mildly Explosive Autoregression with Strong Mixing Errors

In this paper, we consider the mildly explosive autoregression [Formula: see text] , [Formula: see text] , where [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] are arithmetically [Formula: see text]-mixing errors. Under some weak conditions, such as [Formul...

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Autores principales: Liu, Xian, Li, Xiaoqin, Gao, Min, Yang, Wenzhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778470/
https://www.ncbi.nlm.nih.gov/pubmed/36554135
http://dx.doi.org/10.3390/e24121730
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author Liu, Xian
Li, Xiaoqin
Gao, Min
Yang, Wenzhi
author_facet Liu, Xian
Li, Xiaoqin
Gao, Min
Yang, Wenzhi
author_sort Liu, Xian
collection PubMed
description In this paper, we consider the mildly explosive autoregression [Formula: see text] , [Formula: see text] , where [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] are arithmetically [Formula: see text]-mixing errors. Under some weak conditions, such as [Formula: see text] , [Formula: see text] for some [Formula: see text] and mixing coefficients [Formula: see text] , the Cauchy limiting distribution is established for the least squares (LS) estimator [Formula: see text] of [Formula: see text] , which extends the cases of independent errors and geometrically [Formula: see text]-mixing errors. Some simulations for [Formula: see text] , such as the empirical probability of the confidence interval and the empirical density, are presented to illustrate the Cauchy limiting distribution, which have good finite sample performances. In addition, we use the Cauchy limiting distribution of the LS estimator [Formula: see text] to illustrate real data from the NASDAQ composite index from April 2011 to April 2021.
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spelling pubmed-97784702022-12-23 Mildly Explosive Autoregression with Strong Mixing Errors Liu, Xian Li, Xiaoqin Gao, Min Yang, Wenzhi Entropy (Basel) Article In this paper, we consider the mildly explosive autoregression [Formula: see text] , [Formula: see text] , where [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] are arithmetically [Formula: see text]-mixing errors. Under some weak conditions, such as [Formula: see text] , [Formula: see text] for some [Formula: see text] and mixing coefficients [Formula: see text] , the Cauchy limiting distribution is established for the least squares (LS) estimator [Formula: see text] of [Formula: see text] , which extends the cases of independent errors and geometrically [Formula: see text]-mixing errors. Some simulations for [Formula: see text] , such as the empirical probability of the confidence interval and the empirical density, are presented to illustrate the Cauchy limiting distribution, which have good finite sample performances. In addition, we use the Cauchy limiting distribution of the LS estimator [Formula: see text] to illustrate real data from the NASDAQ composite index from April 2011 to April 2021. MDPI 2022-11-26 /pmc/articles/PMC9778470/ /pubmed/36554135 http://dx.doi.org/10.3390/e24121730 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xian
Li, Xiaoqin
Gao, Min
Yang, Wenzhi
Mildly Explosive Autoregression with Strong Mixing Errors
title Mildly Explosive Autoregression with Strong Mixing Errors
title_full Mildly Explosive Autoregression with Strong Mixing Errors
title_fullStr Mildly Explosive Autoregression with Strong Mixing Errors
title_full_unstemmed Mildly Explosive Autoregression with Strong Mixing Errors
title_short Mildly Explosive Autoregression with Strong Mixing Errors
title_sort mildly explosive autoregression with strong mixing errors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778470/
https://www.ncbi.nlm.nih.gov/pubmed/36554135
http://dx.doi.org/10.3390/e24121730
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