Reversed residuals in autoregressive time series analysis
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Authors
Lewis, Peter A. W.
Lawrence, A. J.
Subjects
Linear time series
nonlinear time series
Residuals
Reversed residuals
Nonlinear autoregressive models
Model identification
Partial autocorrelation coefficients
Quadratic partial autocorrelation coefficients
Reversed autoregressive residuals
NEAR(l)
BGAR(l)
nonlinear time series
Residuals
Reversed residuals
Nonlinear autoregressive models
Model identification
Partial autocorrelation coefficients
Quadratic partial autocorrelation coefficients
Reversed autoregressive residuals
NEAR(l)
BGAR(l)
Advisors
Date of Issue
1990-04
Date
1990-04
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
Both linear and nonlinear time series can have directional features, features which indicate that the series do not maintain identical statistical properties when the direction on the time scale is reversed. The main purpose of the present paper is to develop the analysis of these features and to indicate and illustrate how they can be used for the investigation and modelling of linear or nonlinear autoregressive statistical models. In particular, the aim of the paper is to introduce the idea of reversed residuals and to develop some of their properties. Particular pairs of reversed and ordinary residuals are shown to produce partial autocorrelation coefficients: quadratic types of partial autocorrelation coefficients are introduced to assess dependence associated with nonlinear models which nevertheless have linear autoregressive (Yule-Walker) correlation structures. (kr)
Type
Technical Report
Description
Series/Report No
Department
Operations Research
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
NPS-55-90-11
Sponsors
Chief of Naval Research and funded by the Naval Postgraduate School.
Funder
O&MN, Direct Funding
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.