Asymptotic properties of high-order Yule-Walker estimates of the AR parameters of an ARMA time series

The high-order Yule-Walker equations are used to estimate the autoregressive parameters of an autoregressive moving-average time series. The asymptotic statistical properties of these estimates are derived. It is shown that they are asymptotically unbiased and normal, the covariance matrix of the li...

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Bibliographic Details
Published in:IEEE transactions on acoustics, speech, and signal processing speech, and signal processing, 1985-10, Vol.33 (5), p.1095-1101
Main Author: Gingras, D.
Format: Article
Language:English
Subjects:
Additive noise
Applied sciences
Autoregressive processes
Covariance matrix
Econometrics
Equations
Exact sciences and technology
Parameter estimation
Probability
Radar applications
Radiolocalization and radionavigation
Sonar applications
Speech analysis
Telecommunications
Telecommunications and information theory
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Summary:The high-order Yule-Walker equations are used to estimate the autoregressive parameters of an autoregressive moving-average time series. The asymptotic statistical properties of these estimates are derived. It is shown that they are asymptotically unbiased and normal, the covariance matrix of the limit distribution is derived. The special case of estimating the autoregressive parameters of a noise corrupted autoregressive series is also examined.
ISSN:0096-3518
DOI:10.1109/TASSP.1985.1164702