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Inference for bivariate integer-valued moving average models based on binomial thinning operation

Torres, C.

ISLA — Instituto Politécnico de Gestão e Tecnologia

Journal of Applied Statistics, 01 04 2020

Time series of (small) counts are common in practice and appear in a wide variety of fields. In the last three decades, several models that explicitly account for the discreteness of the data have been proposed in the literature. However, for multivariate time series of counts several difficulties arise and the literature is not so detailed. This work considers Bivariate INteger-valued Moving Average, BINMA, models based on the binomial thinning operation. The main probabilistic and statistical properties of BINMA models are studied. Two parametric cases are analysed, one with the cross-correlation generated through a Bivariate Poisson innovation process and another with a Bivariate Negative Binomial innovation process. Moreover, parameter estimation is carried out by the Generalized Method of Moments. The performance of the model is illustrated with synthetic data as well as with real datasets.

Keywords

#binomial thinning #count time series #multivariate count data #cross correlation function #parameter estimation #generalized method of moments #Bivariate analysis #Negative binomial distribution #Mathematics #Statistics #Binomial (polynomial) #Overdispersion #Integer (computer science) #Inference #Econometrics #Multivariate statistics #Probabilistic logic #Poisson distribution #Parametric statistics

Bibliographic Details

Publication Type Journal article

Publication Year 2020

Journal / Conference / Work Journal of Applied Statistics

ISSN 0266-4763

Quartile (SCImago) Q3

Scopus Indexing Indexed source (via SCImago)

ISLA Authors & Affiliations

Inference for bivariate integer-valued moving average models based on binomial thinning operation

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Bibliographic Details

ISLA Authors & Affiliations