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Abstract: An extension of the RINAR1 process for modelling discrete-time dependentcounting processes is considered. The model RINARp investigated here is adirect and natural extension of the real ARp model. Compared to classicalINARp models based on the thinning operator, the new models have severaladvantages: simple innovation structure ; autoregressive coefficients witharbitrary signs ; possible negative values for time series ; possible negativevalues for the autocorrelation function. The conditions for the stationarityand ergodicity, of the RINARp model, are given. For parameter estimation, weconsider the least squares estimator and we prove its consistency undersuitable identifiability condition. Simulation experiments as well as analysisof real data sets are carried out to assess the performance of the model.



Author: M. Kachour

Source: https://arxiv.org/



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