On mixture periodic Integer-Valued <i>ARCH</i> models

Ouzzani, Fares; Bentarzi, Mohamed

doi:10.1080/03610918.2019.1635155

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…The fitted PGPI NGARCH 13 (1, 1) model shows an amelioration comparing to the I NGARCH(1, 13) model (Ferland et al (2006) [1]) and also to the PI NGARCH 13 (1, 1) model (Bentarzi and Bentarzi (2017) [15]), in terms of the Sum of Squared Errors SSE and R 2 results, computed for each model, listed in Table 5. On the other hand, the fitted PGPI NGARCH 13 (1, 1) does not show an improvement compared to the Mixture PI N ARCH 13 (2; 1, 1) model (Ouzzani and Bentarzi (2019) [22]), and this is due to the fact that the series seems to be exhibiting a bimodality. can be improved for a larger size.…”

Section: Empirical Applicationmentioning

confidence: 80%

On Periodic Generalized Poisson INGARCH Models

Aries

2023

The 1st International Online Conference on Mathematics and Applications

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Section: Empirical Applicationmentioning

confidence: 80%

On Periodic Generalized Poisson INGARCH Models

Aries

2023

The 1st International Online Conference on Mathematics and Applications

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“…Additional work on periodic count series is contained in (Moriña et al, 2011; Monteiro et al, 2015; Bentarzi and Bentarzi, 2017; Aknouche et al, 2018; Santos et al, 2021), and Ouzzani and Bentarzi (2019). Most of these references take one of the above approaches.…”

Section: Introductionmentioning

confidence: 99%

Seasonal count time series

Kong

Lund

2022

Journal Time Series Analysis

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Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This article uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm. The model constructed here permits any marginal distribution for the series and the most flexible autocorrelations possible, including those with negative dependence. Likelihood methods of inference are explored. The article first develops the modeling methods, which entail a discrete transformation of a Gaussian process having seasonal dynamics. Properties of this model class are then established and particle filtering likelihood methods of parameter estimation are developed. A simulation study demonstrating the efficacy of the methods is presented and an application to the number of rainy days in successive weeks in Seattle, Washington is given.

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“…Additional work on periodic count series is contained in Moriña et al (2011), Monteiro et al (2015), Bentarzi and Bentarzi (2017), Aknouche et al (2018), Santos et al (2021), Aknouche et al (2018), and Ouzzani and Bentarzi (2019).…”

mentioning

confidence: 99%

Seasonal Count Time Series

Kong¹,

Lund²

2021

Preprint

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Funding informationThe authors thank NSF support from Grant DMS 21-13592.Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This paper uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm. The model permits any marginal distribution for the series and the most flexible autocorrelations possible, including those with negative dependence. Likelihood methods of inference can be conducted and covariates can be easily accommodated. The paper first develops the modeling methods, which entail a discrete transformation of a Gaussian process having seasonal dynamics. Properties of this model class are then established and particle filtering likelihood methods of parameter estimation are developed. A simulation study demonstrating the efficacy of the methods is presented and an application to the number of rainy days in successive weeks in Seattle, Washington is given.

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On mixture periodic Integer-Valued ARCH models

Cited by 13 publications

References 23 publications

On Periodic Generalized Poisson INGARCH Models

On Periodic Generalized Poisson INGARCH Models

Seasonal count time series

Seasonal Count Time Series

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