Brazilian Journal of Probability and Statistics

Estimation of parameters in the $\operatorname{DDRCINAR}(p)$ model

Xiufang Liu and Dehui Wang

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This paper discusses a $p$th-order dependence-driven random coefficient integer-valued autoregressive time series model ($\operatorname{DDRCINAR}(p)$). Stationarity and ergodicity properties are proved. Conditional least squares, weighted least squares and maximum quasi-likelihood are used to estimate the model parameters. Asymptotic properties of the estimators are presented. The performances of these estimators are investigated and compared via simulations. In certain regions of the parameter space, simulative analysis shows that maximum quasi-likelihood estimators perform better than the estimators of conditional least squares and weighted least squares in terms of the proportion of within-$\Omega$ estimates. At last, the model is applied to two real data sets.

Article information

Braz. J. Probab. Stat., Volume 33, Number 3 (2019), 638-673.

Received: March 2018
Accepted: May 2018
First available in Project Euclid: 10 June 2019

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Mathematical Reviews number (MathSciNet)

Conditional least squares maximum quasi-likelihood $\operatorname{DDRCINAR}(p)$ model weighted conditional least squares asymptotic distribution


Liu, Xiufang; Wang, Dehui. Estimation of parameters in the $\operatorname{DDRCINAR}(p)$ model. Braz. J. Probab. Stat. 33 (2019), no. 3, 638--673. doi:10.1214/18-BJPS405.

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