Research on First-Order Time-Varying Random Coefficient Model for Integer-Valued Time Series via the Poisson Thinning Operator
In many instances,integer-valued time series are derived from data gen-erating processes that involve varying parameters and different thinning operators.In this paper,we introduce a first-order non-negative integer-valued autoregressive process,combining the Poisson thinning operator with a time-varying random coef-ficient.We derive the moment condition,stationarity,and ergodicity of this process.Subsequently,we present three estimation methods.To evaluate these methods'per-formance,we employ Monte Carlo simulation results.Additionally,we demonstrate the proposed method's utility through an analysis of data from the Shanghai Stock Exchange,COVID-19 data from Cyprus,and significant annual earthquakes and sat-isfactory results are obtained.
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