Maximum likelihood estimation for all-pass time series models

Beth Andrews, Richard A. Davis and F. Jay Breidt

Journal of Multivariate Analysis, 2006, vol. 97, issue 7, 1638-1659

Abstract: An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximate likelihood for a causal all-pass model is given and used to establish asymptotic normality for maximum likelihood estimators under general conditions. Behavior of the estimators for finite samples is studied via simulation. A two-step procedure using all-pass models to identify and estimate noninvertible autoregressive-moving average models is developed and used in the deconvolution of a simulated water gun seismogram.

Keywords: Gaussian; mixture; Non-Gaussian; Noninvertible; moving; average; White; noise (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (56)

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