Bivariate binomial autoregressive models

Scotto, Manuel G.; Weií, Christian H.; Silva, Maria Eduarda; Pereira, Isabel

doi:10.1016/j.jmva.2013.12.014

Cited by 53 publications

(28 citation statements)

References 38 publications

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“…There is also quite a lot of literature on thinning-based time series models but we are not aware of any publicly available software implementations. To name just a few of many publications, Weiß (2008) reviews univariate time series models based on the thinning operation, Pedeli and Karlis (2013) study a multivariate extension and Scotto, Weiß, Silva, and Pereira (2014) consider models for time series with a finite range of counts. For the wide class of state space models there are the R packages INLA, KFAS and pomp available, although it is quite complex to apply these to count time series.…”

Section: Discussionmentioning

confidence: 99%

tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models

Liboschik¹,

Fokianos²,

Fried³

2017

J. Stat. Soft.

157

163

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The R package tscount provides likelihood-based estimation methods for analysis and modeling of count time series following generalized linear models. This is a flexible class of models which can describe serial correlation in a parsimonious way. The conditional mean of the process is linked to its past values, to past observations and to potential covariate effects. The package allows for models with the identity and with the logarithmic link function. The conditional distribution can be Poisson or negative binomial. An important special case of this class is the so-called INGARCH model and its log-linear extension. The package includes methods for model fitting and assessment, prediction and intervention analysis. This paper summarizes the theoretical background of these methods. It gives details on the implementation of the package and provides simulation results for models which have not been studied theoretically before. The usage of the package is illustrated by two data examples. Additionally, we provide a review of R packages which can be used for count time series analysis. This includes a detailed comparison of tscount to those packages.

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Section: Discussionmentioning

confidence: 99%

tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models

Liboschik¹,

Fokianos²,

Fried³

2017

J. Stat. Soft.

157

163

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“…A different generalization of the binomial thinning operator (1.1) for the bivariate case, based on the bivariate binomial distribution of type II (BVB II ) (Kocherlakota and Kocherlakota, 1992), has been recently introduced by Scotto et al (2014). This new operator has a number of advantages compared to the approach of the previous Section 4.2, see the details below.…”

Section: Bivariate Binomial Thinningmentioning

confidence: 99%

“…This new operator has a number of advantages compared to the approach of the previous Section 4.2, see the details below. Scotto et al (2014) define the bivariate binomial thinning operator as ⊗ X | X ∼ BVB II (X 1 , X 2 , min {X 1 , X 2 };˛1,˛2, ˛) , (4.5)…”

Section: Bivariate Binomial Thinningmentioning

confidence: 99%

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Thinning-based models in the analysis of integer-valued time series: a review

Scotto

Weiß

Gouveia

2015

Statistical Modelling

Self Cite

123

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This article aims at providing a comprehensive survey of recent developments in the field of integer-valued time series modelling, paying particular attention to models obtained as discrete counterparts of conventional autoregressive moving average and bilinear models, and based on the concept of thinning. Such models have proven to be useful in the analysis of many real-world applications ranging from economy and finance to medicine. We review the literature of the most relevant thinning operators proposed in the analysis of univariate and multivariate integer-valued time series with either finite or infinite support. Finally, we also outline and discuss possible directions of future research.

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“…A possible extension is to induce piecewise-type patterns to the class of bivariate binomial autoregressive models introduced by Scotto et al (2014), which are based on the bivariate binomial thinning operator "⊗," defined via…”

Section: A Bivariate Extensionmentioning

confidence: 99%

Self-exciting threshold binomial autoregressive processes

et al. 2015

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We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645-650, 1985. doi:10.1111/j.1752-1688.1985. Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.

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Bivariate binomial autoregressive models

Cited by 53 publications

References 38 publications

tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models

tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models

Thinning-based models in the analysis of integer-valued time series: a review

Self-exciting threshold binomial autoregressive processes

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