The Annals of Statistics

Linear Prediction by Autoregressive Model Fitting in the Time Domain

R. J. Bhansali

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Abstract

Let $\{x_t\}$ be a purely nondeterministic stationary process satisfying all the assumptions made by Berk (1974), and $\{y_t\}$ be another purely nondeterministic stationary process. Assume that $y_t$ is independent of $x_t$ but has exactly the same statistical properties as that of $x_t$. Consider the linear prediction of future values of $y_t$ on the basis of past values, using prediction constants estimated from a realisation of $T$ observations of $x_t$ by least-squares fitting of an autoregression of order $k$. By assuming that $k \rightarrow \infty, k^3/T \rightarrow 0$ as $T \rightarrow \infty$, the effect on the mean square error of prediction of estimating the autoregressive coefficients is determined. This effect is the same as for the case when the prediction constants are estimated by factorising a "windowed" estimate of the spectral density function of $x_t$.

Article information

Source
Ann. Statist., Volume 6, Number 1 (1978), 224-231.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344081

Digital Object Identifier
doi:10.1214/aos/1176344081

Mathematical Reviews number (MathSciNet)
MR461824

Zentralblatt MATH identifier
0383.62061

JSTOR
links.jstor.org

Subjects
Primary: 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Keywords
Autoregressive process time domain Yule-Walker equations time series

Citation

Bhansali, R. J. Linear Prediction by Autoregressive Model Fitting in the Time Domain. Ann. Statist. 6 (1978), no. 1, 224--231. doi:10.1214/aos/1176344081. https://projecteuclid.org/euclid.aos/1176344081


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