A seasonal geometric INAR process based on negative binomial thinning operator

Shengqi Tian (), Dehui Wang and Shuai Cui ()
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Shengqi Tian: Jilin University
Dehui Wang: Jilin University
Shuai Cui: Jilin University

Statistical Papers, 2020, vol. 61, issue 6, No 13, 2581 pages

Abstract: Abstract In this article, we propose a new seasonal geometric integer-valued autoregressive process based on the negative binomial thinning operator with seasonal period s. Some basic probabilistic and statistical properties of the model are discussed. Conditional maximum likelihood estimators are obtained, and the asymptotic properties of the estimators are established. Some theoretical results of point forecasts are obtained. Numerical results are presented. At the end, two real data examples are investigated to assess the performance of our new model.

Keywords: Seasonality; Over-dispersion; Negative binomial thinning operator; Geometric distribution; Estimate; Forecast (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-018-1060-7

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