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Interrupted Time Series Analysis$
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David McDowall, Richard McCleary, and Bradley J. Bartos

Print publication date: 2019

Print ISBN-13: 9780190943943

Published to Oxford Scholarship Online: February 2021

DOI: 10.1093/oso/9780190943943.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use.date: 06 July 2022

The Noise Component:N(at)

The Noise Component:N(at)

Chapter:
(p.48) 3 The Noise Component:N(at)
Source:
Interrupted Time Series Analysis
Author(s):

David McDowall

Richard McCleary

Bradley J. Bartos

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190943943.003.0003

Chapter 3 develops the methods or strategies for building ARIMA noise models. At one level, the iterative identify-estimate-diagnose modeling strategy proposed by Box and Jenkins has changed little. At another level, the collective experience of time series experimenters leads to several modifications of the strategy. For the most part, these changes are aimed at solving practical problems. Compared to the 1970s, for example, modelers today pay more attention to transformations and to the usefulness and interpretability of an ARIMA model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models build in Chapter 3 are re-introduced in later chapters.

Keywords:   Box and Jenkins, Kolomogov-Smirnov (KS) test, sample autocorrelation function (ACF), sample partial autocorrelation function (PACF), stationarity-invertibility bounds, identification-estimation-diagnosis

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