Higher order residual analysis for nonlinear time series with autoregressive correlation structures
By: Peter A.W. Lewis, Anthony J. Lawrence
Contributors: Lawrence, A. J.; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1985
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 23
Subjects: NONLINEAR ANALYSIS
Other titles: NPS-55-85-030.
Description: The paper considers nonlinear time series whose second order autocorrelations satisfy      autoregressive Yule-Walker equations. The usual linear residuals are then uncorrelated, but      not independent, as would be the case for linear autoregressive processes. Two such types of      nonlinear model are treated in some detail: random coefficient autoregression and multiplicative      autoregression. The proposed analysis involves crosscorrelation of the usual linear residuals      and their squares. This function is obtained for the two types of model considered, and allows      differentiation between models with the same autocorrelation structure in the same class. For      the random coefficient models it is shown that one side of the crosscorrelation function is zero,      giving a useful signature of thes processes. The non-zero features of the other side of the      crosscorrelations are informative of the higher order dependency structure. In applications this      residual analysis requires only standard statistical calculations, and extends rather than replaces      the usual second order analysis. Keywords: Nonlinear time series; Autoregressive; Linear      residuals; Random coefficient autoregression; Multiplicative autoregression; Residual analysis.
Notes: Title from cover.

"NPS-55-85-030."

"December 1985.

AD A163 611.

Includes bibliographical references (p. 22-23).

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