Linear Prediction of Long-Memory Processes: Asymptotic Results on Mean-squared Errors
| dc.creator | Godet, Fanny | |
| dc.date | 2007-05-14 | |
| dc.date.accessioned | 2026-07-07T08:01:25Z | |
| dc.date.available | 2026-07-07T08:01:25Z | |
| dc.description | We present two approaches for linear prediction of long-memory time series. The first approach consists in truncating the Wiener-Kolmogorov predictor by restricting the observations to the last $k$ terms, which are the only available values in practice. We derive the asymptotic behaviour of the mean-squared error as $k$ tends to $ + \infty$. By contrast, the second approach is non-parametric. An AR($k$) model is fitted to the long-memory time series and we study the error that arises in this misspecified model. | |
| dc.identifier | https://arxiv.org/abs/0705.1927 | |
| dc.identifier | http://arxiv.org/abs/0705.1927 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/128902 | |
| dc.subject | Statistics Theory | |
| dc.title | Linear Prediction of Long-Memory Processes: Asymptotic Results on Mean-squared Errors | |
| dc.type | text |