Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes

This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood infere...
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Autor*in:	Congmin Liu [verfasserIn] Jianhua Cheng [verfasserIn] Dehui Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	periodic autoregression integer-valued threshold models parameter estimation

Übergeordnetes Werk:	In: Entropy - MDPI AG, 2003, 23(2021), 6, p 765
Übergeordnetes Werk:	volume:23 ; year:2021 ; number:6, p 765

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DOI / URN:	10.3390/e23060765

Katalog-ID:	DOAJ030294762

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520			\|a This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed.
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allfieldsGer	10.3390/e23060765 doi (DE-627)DOAJ030294762 (DE-599)DOAJ26028a4c2641463f85ecbe82008129da DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Congmin Liu verfasserin aut Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed. periodic autoregression integer-valued threshold models parameter estimation Science Q Astrophysics Physics Jianhua Cheng verfasserin aut Dehui Wang verfasserin aut In Entropy MDPI AG, 2003 23(2021), 6, p 765 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:23 year:2021 number:6, p 765 https://doi.org/10.3390/e23060765 kostenfrei https://doaj.org/article/26028a4c2641463f85ecbe82008129da kostenfrei https://www.mdpi.com/1099-4300/23/6/765 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2021 6, p 765
allfieldsSound	10.3390/e23060765 doi (DE-627)DOAJ030294762 (DE-599)DOAJ26028a4c2641463f85ecbe82008129da DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Congmin Liu verfasserin aut Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed. periodic autoregression integer-valued threshold models parameter estimation Science Q Astrophysics Physics Jianhua Cheng verfasserin aut Dehui Wang verfasserin aut In Entropy MDPI AG, 2003 23(2021), 6, p 765 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:23 year:2021 number:6, p 765 https://doi.org/10.3390/e23060765 kostenfrei https://doaj.org/article/26028a4c2641463f85ecbe82008129da kostenfrei https://www.mdpi.com/1099-4300/23/6/765 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2021 6, p 765
language	English
source	In Entropy 23(2021), 6, p 765 volume:23 year:2021 number:6, p 765
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topic_facet	periodic autoregression integer-valued threshold models parameter estimation Science Q Astrophysics Physics
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authorswithroles_txt_mv	Congmin Liu @@aut@@ Jianhua Cheng @@aut@@ Dehui Wang @@aut@@
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callnumber-first	Q - Science
author	Congmin Liu
spellingShingle	Congmin Liu misc QB460-466 misc QC1-999 misc periodic autoregression misc integer-valued threshold models misc parameter estimation misc Science misc Q misc Astrophysics misc Physics Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes
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topic_title	QB460-466 QC1-999 Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes periodic autoregression integer-valued threshold models parameter estimation
topic	misc QB460-466 misc QC1-999 misc periodic autoregression misc integer-valued threshold models misc parameter estimation misc Science misc Q misc Astrophysics misc Physics
topic_unstemmed	misc QB460-466 misc QC1-999 misc periodic autoregression misc integer-valued threshold models misc parameter estimation misc Science misc Q misc Astrophysics misc Physics
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title_auth	Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes
abstract	This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed.
abstractGer	This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed.
abstract_unstemmed	This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed.
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title_short	Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes
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Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes

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