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August 2019 Estimation of parameters in the $\operatorname{DDRCINAR}(p)$ model
Xiufang Liu, Dehui Wang
Braz. J. Probab. Stat. 33(3): 638-673 (August 2019). DOI: 10.1214/18-BJPS405

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.

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Xiufang Liu. Dehui Wang. "Estimation of parameters in the $\operatorname{DDRCINAR}(p)$ model." Braz. J. Probab. Stat. 33 (3) 638 - 673, August 2019. https://doi.org/10.1214/18-BJPS405

Information

Received: 1 March 2018; Accepted: 1 May 2018; Published: August 2019
First available in Project Euclid: 10 June 2019

MathSciNet: MR3960279
Digital Object Identifier: 10.1214/18-BJPS405

Rights: Copyright © 2019 Brazilian Statistical Association

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Vol.33 • No. 3 • August 2019
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