Comparing Model of Air Pollution Index Using Generalized Autoregressive Conditional Heteroskedasticity Family (GARCH)

Abstract

The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environmental issue concern. In this paper, the daily integer value time series data for API in Penang and 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 are analysed. The parameters of the models will be estimated using quasi likelihood estimator (QLE) and compared their Akaike information criterion (AIC) to determine the best model fitted the data. The results showed that INGARCH (1,1) model will be the best model because it has the small value of AIC. Hence, the findings are very important for controlling the API results in the future and taking protective measures for the conservation of the air.