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<p>In this paper, we propose a new class of binomial autoregressive models with time-varying parameters, where the parameters are driven by stochastic recurrence equations and can effectively capture changing dependence over time. We consider the conditional maximum likelihood estimators and derive the related asymptotic properties. Furthermore, we give the consistency of the plug-in estimators for the conditional probability mass function. In the simulation study, we show the reliability of the estimators. Finally, two real data examples in the fields of meteorology and epidemiology are analyzed to illustrate our model.</p>

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Statistical inference for the binomial autoregressive model with time-varying parameters

  • 期刊论文

  • 发表日期：

    2025年2月27日

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  • Rui Zhang,
  • Xiaogang Dong

摘要

In this paper, we propose a new class of binomial autoregressive models with time-varying parameters, where the parameters are driven by stochastic recurrence equations and can effectively capture changing dependence over time. We consider the conditional maximum likelihood estimators and derive the related asymptotic properties. Furthermore, we give the consistency of the plug-in estimators for the conditional probability mass function. In the simulation study, we show the reliability of the estimators. Finally, two real data examples in the fields of meteorology and epidemiology are analyzed to illustrate our model.