Bivariate binomial autoregressive models

Manuel G. Scotto, Christian H. Weiß, Maria Eduarda Silva and Isabel Pereira

Journal of Multivariate Analysis, 2014, vol. 125, issue C, 233-251

Abstract: This paper introduces new classes of bivariate time series models being useful to fit count data time series with a finite range of counts. Motivation comes mainly from the comparison of schemes for monitoring tourism demand, stock data, production and environmental processes. All models are based on the bivariate binomial distribution of Type II. First, a new family of bivariate integer-valued GARCH models is proposed. Then, a new bivariate thinning operation is introduced and explained in detail. The new thinning operation has a number of advantages including the fact that marginally it behaves as the usual binomial thinning operation and also that allows for both positive and negative cross-correlations. Based upon this new thinning operation, a bivariate extension of the binomial autoregressive model of order one is introduced. Basic probabilistic and statistical properties of the model are discussed. Parameter estimation and forecasting are also covered. The performance of these models is illustrated through an empirical application to a set of rainy days time series collected from 2000 up to 2010 in the German cities of Bremen and Cuxhaven.

Keywords: Bivariate binomial distribution; Binomial AR(1) model; INAR(1) model; INGARCH model; Thinning operation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.jmva.2013.12.014

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