Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 32, Issue 9, Pages 2495-2514Publisher
SPRINGER
DOI: 10.1007/s00477-018-1584-3
Keywords
Binomial variation; Count time series; ARMA structure; Rainy-days time series
Categories
Funding
- German Academic Exchange Service (DAAD)
- Fundacao para a Ciencia e a Tecnologia (FCT), under the program Acoes Integradas Luso-Alemas
- FCT
- national (MEC) and European structural funds through the programs FEDER under PT2020 within IEETA/UA project [UID/CEC/00127/2013]
- CIDMA/UA project [UID/MAT/04106/2013]
- FCT [SFRH/BPD/87037/2012]
- [57212119]
- [A-38/16]
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Motivated by a large dataset containing time series of weekly number of rainy days collected over two thousand locations across Europe and Russia for the period 2000-2010, we propose a new class of ARMA-like model for time series of bounded counts, which can also handle extra-binomial variation. We abbreviate this model as bvARMA, as it is based upon a novel operation referred to as binomial variation. After having discussed important stochastic properties and proposed a model-fitting approach relying on maximum likelihood estimation, we apply the bvARMA model family to the rainy-days time series. Results show that both bvAR and bvMA models are adequate and exhibit a similar performance. Furthermore, bvARMA results outperform those obtained by fitting ordinary discrete ARMA (NDARMA) models of the same order.
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