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Activity Number:
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65
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 12, 2002 : 8:30 AM to 10:20 AM
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Sponsor:
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Business & Economics Statistics Section*
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| Abstract - #300778 |
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Title:
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Noninvertible Moving Average Modeling Via All-Pass Filters
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Author(s):
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Jay Breidt*+ and Richard Davis and Beth Andrews
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Affiliation(s):
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Colorado State University and Colorado State University and Colorado State University
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Address:
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201 Statistics, Fort Collins, Colorado, 80523-1877, USA
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Keywords:
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non-Gaussian time series ; quasi-likelihood ; least absolute deviations
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Abstract:
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All-pass models are autoregressive-moving average models in which all of the roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. If an all-pass is driven with heavy-tailed noise, then its marginal distribution will also have heavy tails, and the process will exhibit "volatility clustering" much like a nonlinear model such as ARCH. All-pass models are useful in identifying and estimating noninvertible moving averages. Theoretical properties of alternative estimation procedures for all-pass, based on a likelihood approximation and related criterion functions, are described. Behavior of the estimators in finite samples is studied via simulation.
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