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Unified inference for an integer-valued AR(1) model

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  • Longyu Chen
  • Xiaohui Liu
  • Liang Peng
  • Fukang Zhu

Abstract

Conditional least squares estimation is often employed to infer an integer-valued AR(1) model and its convergence rate and asymptotic variance differ for the stable and nearly unstable cases. This article adopts a random weighted bootstrap method to provide a unified interval estimation and hypothesis test regardless of the underlying process being either stable or nearly unstable. A simulation study confirms the good finite sample performance of the proposed inference. We also apply it to test for a unit root test in a COVID-19 dataset.

Suggested Citation

  • Longyu Chen & Xiaohui Liu & Liang Peng & Fukang Zhu, 2024. "Unified inference for an integer-valued AR(1) model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(12), pages 3732-3742, October.
    • Handle: RePEc:taf:lstaxx:v:54:y:2024:i:12:p:3732-3742
    DOI: 10.1080/03610926.2024.2403547
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