An empirical-likelihood-based structural-change test for INAR processes

Abstract

The empirical likelihood ratio (ELR) test is proposed for uncovering a structural change in integer-valued autoregressive (INAR) processes. The limiting distribution is derived under the null hypothesis that the parameter did not change at the anticipated change points. To evaluate the finite-sample performance of the proposed ELR test, the empirical sizes and powers are investigated in a simulation study. The ELR test is also applied to real data on infectious disease and crime counts.

Keywords:

• Count time series
• COVID-19
• empirical likelihood
• INAR model
• parameter change test

Acknowledgments

The authors thank the referee for useful comments on an earlier draft of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.nippon.com/en/japan-data/h00443/japan/%E2/%80/%99s-national-holidays-in-2020.html

2 https://pittsburghpa.gov/omb/budgets-reports/#

3 http://www.forecastingprinciples.com/

Additional information

Funding

This research was supported by the National Social Science Foundation of China (No. 18BTJ039).