A time series model: First-order integer-valued autoregressive (INAR(1))

Simarmata, Djamester; Novkaniza, Fevi; Widyaningsih, Yekti

doi:10.1063/1.4991261

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Bayesian Forecasting Methods1

Prediction In the Plinar(1) Process1

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2020

2024

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Cited by 2 publications

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“…The Bayesian predictive probability function is a weighted average over the parameter space Θ, and as a posterior distribution, it assigns a weight to every possible parameter setting. Silva et al (2009) and Simarmata et al (2017) suggested a Bayesian methodology for Poisson INAR(1) process. Here, we extend their method to PLINAR(1) process.…”

Section: Bayesian Forecasting Methodsmentioning

confidence: 99%

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Poisson-Lindley INAR(1) Processes: Some Estimation and Forecasting Methods

Nasirzadeh

Ahmad

2020

JIRSS

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This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predicted value for h-step ahead of the series is obtained. Some simulations and a real data analysis are applied to compare the presented estimations and the prediction methods.

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Section: Bayesian Forecasting Methodsmentioning

confidence: 99%

“…Note that if f X t+h |X t (0|x t ) > 0.5, the median is not defined and median forecast method cannot be applied. (See, Simarmata et al (2017)). As we can see in the Anorexia data set, Table (7) in Section 7, in PLINAR(1) the chance of zero is high, so the MTP method is not an appropriate method.…”

Section: Prediction In the Plinar(1) Processmentioning

confidence: 99%