An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion

Marcelo Bourguignon () and Christian H. Weiß ()
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Marcelo Bourguignon: Universidade Federal Do Rio Grande Do Norte
Christian H. Weiß: Helmut Schmidt University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 4, No 12, 847-868

Abstract: Abstract We present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data.

Keywords: INAR(1) process; Bernoulli distribution; Geometric distribution; Integer-valued time series; Binomial thinning; Negative binomial thinning; 60J10; 62M05; 62M10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11749-017-0536-4

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