Journal
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
Volume 48, Issue 2, Pages 248-264Publisher
KOREAN STATISTICAL SOC
DOI: 10.1016/j.jkss.2018.11.004
Keywords
Random coefficient INAR(1) models; Negative binomial thinning; Conditional least squares; Empirical likelihood
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Funding
- National Natural Science Foundation of China [11871028, 11271155, 11371168, J1310022, 11571138, 11501241, 11571051, 11301137]
- National Social Science Foundation of China [16BTJ020]
- Science and Technology Research Program of Education Department in Jilin Province for the 12th Five-Year Plan, China [440020031139]
- Jilin Province Natural Science Foundation, China [20150520053JH]
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Integer-valued time series models and their applications have attracted a lot of attention over the last years. In this paper, we introduce a class of observation-driven random coefficient integer-valued autoregressive processes based on negative binomial thinning, where the autoregressive parameter depends on the observed values of the previous moment. Basic probability and statistics properties of the process are established. The unknown parameters are estimated by the conditional least squares and empirical likelihood methods. Specially, we consider three aspects of the empirical likelihood method: maximum empirical likelihood estimate, confidence region and EL test. The performance of the two estimation methods is compared through simulation studies. Finally, an application to a real data example is provided. (C) 2018 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
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