Abstract
In this article we propose a quasi-Whittle estimator for parametric families of time series models in the presence of missing data. This estimator extends results to the incompletely observed case. This extension is valid to non-Gaussian and nonlinear models. It also allows us to bound the variance of an associated quasiperi-odogram. A simulation study empirically validates the proposed estimate for mixing and nonmixing models.
Original language | English |
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Pages (from-to) | 59-79 |
Number of pages | 21 |
Journal | Theory of Probability and Mathematical Statistics |
Volume | 95 |
DOIs | |
State | Published - 2017 |
Keywords
- Auto correlation
- Limit theorems
- Time series
- Weakly dependent
- Whittle estimator