Quadratic estimating functions for Nbingarch model

Time series of counts has been widely used in many real-world applications. In this paper, we derive the quadratic estimating functions for negative binomial GARCH, known as NBINGARCH (p,q) model. Specifically, we derive the optimal function of NBINGARCH(1,1) and obtain the estimated parameters of i...

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Main Authors: Mohamed, Ibrahim, Mohamad, Nurul Najihah, Thavaneswaran, A., Ng, Kok Hau
Format: Proceeding Paper
Language:English
Published: 2019
Subjects:
Online Access:http://irep.iium.edu.my/81077/
http://irep.iium.edu.my/81077/7/81077%20program%20book%20and%20abstract.pdf
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author Mohamed, Ibrahim
Mohamad, Nurul Najihah
Thavaneswaran, A.
Ng, Kok Hau
author_facet Mohamed, Ibrahim
Mohamad, Nurul Najihah
Thavaneswaran, A.
Ng, Kok Hau
author_sort Mohamed, Ibrahim
building IIUM Repository
collection Online Access
description Time series of counts has been widely used in many real-world applications. In this paper, we derive the quadratic estimating functions for negative binomial GARCH, known as NBINGARCH (p,q) model. Specifically, we derive the optimal function of NBINGARCH(1,1) and obtain the estimated parameters of interest via simulation. We show that the performance of the quadratic estimating functions method is superior compared to estimating functions and maximum likelihood methods. For illustration, we fit the NBINGARCH(1,1) on the poliomyelitis cases in the United State from 1970 to 1983.
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format Proceeding Paper
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institution International Islamic University Malaysia
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language English
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publishDate 2019
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spelling iium-810772020-07-16T02:32:39Z http://irep.iium.edu.my/81077/ Quadratic estimating functions for Nbingarch model Mohamed, Ibrahim Mohamad, Nurul Najihah Thavaneswaran, A. Ng, Kok Hau QA276 Mathematical Statistics Time series of counts has been widely used in many real-world applications. In this paper, we derive the quadratic estimating functions for negative binomial GARCH, known as NBINGARCH (p,q) model. Specifically, we derive the optimal function of NBINGARCH(1,1) and obtain the estimated parameters of interest via simulation. We show that the performance of the quadratic estimating functions method is superior compared to estimating functions and maximum likelihood methods. For illustration, we fit the NBINGARCH(1,1) on the poliomyelitis cases in the United State from 1970 to 1983. 2019 Proceeding Paper NonPeerReviewed application/pdf en http://irep.iium.edu.my/81077/7/81077%20program%20book%20and%20abstract.pdf Mohamed, Ibrahim and Mohamad, Nurul Najihah and Thavaneswaran, A. and Ng, Kok Hau (2019) Quadratic estimating functions for Nbingarch model. In: 62nd ISI World Statistics Congress 2019, 18th until 23rd August 2019, Kuala Lumpur. (Unpublished)
spellingShingle QA276 Mathematical Statistics
Mohamed, Ibrahim
Mohamad, Nurul Najihah
Thavaneswaran, A.
Ng, Kok Hau
Quadratic estimating functions for Nbingarch model
title Quadratic estimating functions for Nbingarch model
title_full Quadratic estimating functions for Nbingarch model
title_fullStr Quadratic estimating functions for Nbingarch model
title_full_unstemmed Quadratic estimating functions for Nbingarch model
title_short Quadratic estimating functions for Nbingarch model
title_sort quadratic estimating functions for nbingarch model
topic QA276 Mathematical Statistics
url http://irep.iium.edu.my/81077/
http://irep.iium.edu.my/81077/7/81077%20program%20book%20and%20abstract.pdf