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Generalized Poisson Difference Autoregressive Processes

Giulia Carallo, Roberto Casarin, Christian P. Robert

arXiv 11 Feb 2020 · Statistics — Methodology · publishedInternational Journal of Forecasting (2023) · 10 citations (OpenAlex)

arXiv:2002.04470 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper introduces a new stochastic process with values in the set Z of integers with sign. The increments of process are Poisson differences and the dynamics has an autoregressive structure. We study the properties of the process and exploit the thinning representation to derive stationarity conditions and the stationary distribution of the process. We provide a Bayesian inference method and an efficient posterior approximation procedure based on Monte Carlo. Numerical illustrations on both simulated and real data show the effectiveness of the proposed inference.

Citation extraction

93
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157
in-text mentions
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distinct cited
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Ferland, R., Latour, A., and Oraichi, D (2006) Integer-valued GARCH process0.96510490%
2Zhu, F (2012) Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models0.8947371%
3Cunha, E. T. d., Vasconcellos, K. L., and Bourguignon, M (2018) A skew integer-valued time-series process with generalized Poisson difference marginal distribution0.87452100%
4Wei, C. H (2008) Thinning operations for modeling time series of counts? A survey0.8434375%
5Consul, P. C (1986) On the differences of two generalized Poisson variates0.8435460%
6Alzaid, A. and Al-Osh, M (1993) Generalized Poisson ARMA processes0.8307357%
7Kim, H.-Y. and Park, Y (2008) A non-stationary integer-valued autoregressive model0.81142100%
8Wei, C. H (2009) Modelling time series of counts with overdispersion0.64441100%
9Agrafiotis, I., Nurse, J. R. C., Goldsmith, M., Creese, S., and Upto… (2018) A taxonomy of cyber-harms: Defining the impacts of cyber-attacks and understanding how they propagate0.64422100%
10Andersson, J. and Karlis, D (2014) A parametric time series model with covariates for integers in Z0.64422100%

Showing the top 10 of 93 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1A Dynamic Stochastic Block Model for Multidimensional Networks0.40511