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This paper brings forward a  p th-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (MDDRCINAR( p )). Stationarity and ergodicity properties of the proposed model are derived. The unknown parameters are estimated by conditional least squares, weighted least squares and maximum quasi-likelihood and asymptotic characterization of the obtained parameter estimators is proved. The performances of the proposed estimate methods are checked via simulations, which present that maximum quasi-likelihood estimators perform better than the other two estimate methods considering the proportion of within-Ω estimates in certain regions of the parameter space. The applicability of the model is investigated using two real count data sets.

Estimation of parameters in the MDDRCINAR( p ) model