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Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).

The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroske...

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Main Authors: Zamrus, Nurul Asyikin, Mohd Rodzhan, Mohd Hirzie, Mohamad, Nurul Najihah
Format: Article
Language:English
Published: UTM Press 2022
Subjects:
QA276 Mathematical Statistics
Online Access:http://irep.iium.edu.my/119183/17/119183_Forecasting%20model%20of%20air%20pollution%20index.pdf
http://irep.iium.edu.my/119183/
https://mjfas.utm.my/index.php/mjfas/article/view/2279
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spelling my.iium.irep.1191832025-02-10T01:55:06Z http://irep.iium.edu.my/119183/ Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH). Zamrus, Nurul Asyikin Mohd Rodzhan, Mohd Hirzie Mohamad, Nurul Najihah QA276 Mathematical Statistics The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroskedasticity (GARCH) family for discrete case namely Poisson integer value GARCH (INGARCH), negative binomial integer value GARCH (NBINGARCH) and integer value autoregressive conditional heteroskedasticity (INARCH) models. The parameters of the models will be estimated using quasi likelihood estimator (QLE) and we compare their Aiken information criterion (AIC) and Bayesian information criteria (BIC) to determine the best model fitted the data. Besides, the forecasting performance will be measured by using mean square error (MSE) and Pearson Standard Error (et). The results showed that INGARCH (1,1) and INARCH (1,0) performed inconsistent results since the conventional methods of NBINGARCH (1,1) outperformed the performance of INGARCH (1,1) and INARCH (1,0). However, consistent results were achieved as the NBINGARCH (1,1) gave the smallest forecasting error compared to INGARCH (1,1) and INARCH (1,0). The findings are very important for controlling the API results in future and taking protection measure for conservation of the air UTM Press 2022-05-16 Article PeerReviewed application/pdf en http://irep.iium.edu.my/119183/17/119183_Forecasting%20model%20of%20air%20pollution%20index.pdf Zamrus, Nurul Asyikin and Mohd Rodzhan, Mohd Hirzie and Mohamad, Nurul Najihah (2022) Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH). Malaysian Journal of Fundamental and Applied Sciences, 18 (2). pp. 184-196. ISSN 2289-5981 E-ISSN 2289-599X https://mjfas.utm.my/index.php/mjfas/article/view/2279 10.11113/mjfas.v18n2.2279
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA276 Mathematical Statistics
spellingShingle QA276 Mathematical Statistics
Zamrus, Nurul Asyikin
Mohd Rodzhan, Mohd Hirzie
Mohamad, Nurul Najihah
Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
description The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroskedasticity (GARCH) family for discrete case namely Poisson integer value GARCH (INGARCH), negative binomial integer value GARCH (NBINGARCH) and integer value autoregressive conditional heteroskedasticity (INARCH) models. The parameters of the models will be estimated using quasi likelihood estimator (QLE) and we compare their Aiken information criterion (AIC) and Bayesian information criteria (BIC) to determine the best model fitted the data. Besides, the forecasting performance will be measured by using mean square error (MSE) and Pearson Standard Error (et). The results showed that INGARCH (1,1) and INARCH (1,0) performed inconsistent results since the conventional methods of NBINGARCH (1,1) outperformed the performance of INGARCH (1,1) and INARCH (1,0). However, consistent results were achieved as the NBINGARCH (1,1) gave the smallest forecasting error compared to INGARCH (1,1) and INARCH (1,0). The findings are very important for controlling the API results in future and taking protection measure for conservation of the air
format Article
author Zamrus, Nurul Asyikin
Mohd Rodzhan, Mohd Hirzie
Mohamad, Nurul Najihah
author_facet Zamrus, Nurul Asyikin
Mohd Rodzhan, Mohd Hirzie
Mohamad, Nurul Najihah
author_sort Zamrus, Nurul Asyikin
title Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
title_short Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
title_full Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
title_fullStr Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
title_full_unstemmed Forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (GARCH).
title_sort forecasting model of air pollution index using generalized autoregressive conditional heteroskedasticity family (garch).
publisher UTM Press
publishDate 2022
url http://irep.iium.edu.my/119183/17/119183_Forecasting%20model%20of%20air%20pollution%20index.pdf
http://irep.iium.edu.my/119183/
https://mjfas.utm.my/index.php/mjfas/article/view/2279
_version_ 1827438742091071488
score 13.251813

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