Estimation of parameters in the \(\mathrm{DDRCINAR}(p)\) model. (English) Zbl 1426.62261
Summary: This paper discusses a \(p\)th-order dependence-driven random coefficient integer-valued autoregressive time series model \((\mathrm{DDRCINAR}(p))\). 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.
MSC:
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 62F12 | Asymptotic properties of parametric estimators |
| 62E20 | Asymptotic distribution theory in statistics |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
conditional least squares; maximum quasi-likelihood; \(\mathrm{DDRCINAR}(p)\) model; weighted conditional least squares; asymptotic distribution; time series modelReferences:
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