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

Isabel Silva, Maria Eduarda Silva and Cristina Torres

Journal of Applied Statistics, 2020, vol. 47, issue 13-15, 2546-2564

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2546-2564

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DOI: 10.1080/02664763.2020.1747411

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