## Brazilian Journal of Probability and Statistics

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

#### Abstract

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

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

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

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1560153855

Digital Object Identifier
doi:10.1214/18-BJPS405

Mathematical Reviews number (MathSciNet)
MR3960279

#### Citation

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. https://projecteuclid.org/euclid.bjps/1560153855

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