Estimation of parameters in the MDDRCINAR(p) model

ABSTRACT

This paper brings forward a pth-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.

KEYWORDS:

• Conditional least squares
• weighted least squares
• maximum quasi-likelihood
• MDDRCINAR(p) model
• asymptotic distribution

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Acknowledgements

The authors are very grateful to editor and two referees for their careful reading and valuable comments which have greatly improved this paper. Thank you all for helping me writing this LaTeX sample file.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by National Natural Science Foundation of China [No. 11871028, 11731015].