Higher order residual analysis for nonlinear time series with autoregressive correlation structures
By: by P.A.W. Lewis and A.J. Lawrance
Contributors: Lawrance, A. J.; Naval Postgraduate School (U.S.)
Publisher: Naval Postgraduate School
Published: 1984
Place of publication: Monterey, California
Language: eng
Number of pages: 24
Subjects: Time-series analysis; Statistical analysis
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 these processes. The non-zero features 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.
Notes: Title from cover.

"Prepared for: Chief of Naval Research, Arlington, VA 22217"--Cover.

"September 1984"--Cover.

"NPS55-84-022"--Cover.

DTIC Identifiers: Yule Walker equations, PE61153N.

Includes bibliographical references (p. 23-24).

"Approved for public release; distribution unlimited"--Cover.

Technical report; 1984.

ck/ 9/22/09.