An integer-valued threshold autoregressive process based on negative binomial thinning

Kai Yang, Dehui Wang, Boting Jia and Han Li
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Kai Yang: Institute of Mathematics Jilin University
Dehui Wang: Institute of Mathematics Jilin University
Boting Jia: School of Statistics Jilin University of Finance and Economics
Han Li: Institute of Mathematics Jilin University

Statistical Papers, 2018, vol. 59, issue 3, No 15, 1160 pages

Abstract: Abstract In this paper, we introduce an integer-valued threshold autoregressive process, which is driven by independent negative-binomial distributed random variables and based on negative binomial thinning. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators and corresponding iterative algorithms are investigated for both the cases that the threshold variable is known or not. Also, the asymptotic properties of the estimators are obtained. Finally, some numerical results of the estimates and a real data example are presented.

Keywords: Integer-valued threshold models; Min-Min algorithm; Negative binomial thinning; Estimation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00362-016-0808-1

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