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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: 26 January 2022

Auxiliary Modeling Procedures

Auxiliary Modeling Procedures

Chapter:
(p.136) 5 Auxiliary Modeling Procedures
Source:
Interrupted Time Series Analysis
Author(s):

David McDowall

Richard McCleary

Bradley J. Bartos

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190943943.003.0005

Chapter 5 describes three sets of auxiliary methods that have emerged as add-on supplements to the traditional ARIMA model-building strategy. First, Bayesian information criteria (BIC) can be used to inform incremental modeling decisions. BICs are also the basis for the Bayesian hypothesis tests introduced in Chapter 6. Second, unit root tests can be used to inform differencing decisions. Used appropriately, unit root tests guard against over-differencing. Finally, co-integration and error correction models have become a popular way of representing the behavior of two time series that follow a shared path. We use the principle of co-integration to define the ideal control time series. Put simply, a time series and its ideal counterfactual control time series are co-integrated up the time of the intervention. At that point, if the two time series diverge, the magnitude of their divergence is taken as the causal effect of the intervention.

Keywords:   Bayes factor, Bayesian significance testing, unit-root test, Augmented Dickey-Fuller co-integration, ideal control time series, error correction mechanism

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