1 edition of **Reversed residuals in autoregressive time series analysis** found in the catalog.

Reversed residuals in autoregressive time series analysis

Peter A. W. Lewis

- 49 Want to read
- 31 Currently reading

Published
**1990** by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va .

Written in English

- TIME SERIES ANALYSIS,
- RESIDUALS,
- STATISTICAL ANALYSIS

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)

**Edition Notes**

Other titles | NPS-55-90-11. |

Statement | Peter A.W. Lewis, and A.J. Lawrence |

Contributions | Lawrence, A. J., Naval Postgraduate School (U.S.). Dept. of Operations Research |

The Physical Object | |
---|---|

Pagination | i, 12 p. : |

Number of Pages | 12 |

ID Numbers | |

Open Library | OL25491950M |

Q&A for finance professionals and academics. Stack Exchange network consists of Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.. Visit Stack Exchange. GARCH (generalized autoregressive conditional heteroscedastic) model. We first model the return series with the classical GARCH(1,1) model (1) where is a sequence of i.i.d. (independently identically distributed) random variables. We often assume it obeys a normal distribution. is the mean of the return series. is the time-varying volatility.

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