Linear Prediction of Long-Memory Processes: Asymptotic Results on Mean-squared Errors

Fanny Godet

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arXiv · 0705.1927

Linear Prediction of Long-Memory Processes: Asymptotic Results on Mean-squared Errors

Fanny Godet

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Abstract

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.

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Fanny Godet. 2007-05-14. Linear Prediction of Long-Memory Processes: Asymptotic Results on Mean-squared Errors. https://arxiv.org/abs/0705.1927

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