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Interrupted Time Series Analysis

David McDowall, Richard McCleary, and Bradley J. Bartos

Abstract

Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread a ... More

Keywords: time series, Rubin causality, causal inference, ARIMA models, Box-Jenkins-Tiao strategy, statistical conclusion validity, counterfactual control, synthetic control time designs, Box-Cox transformation, Bayesian hypothesis test

Bibliographic Information

Print publication date: 2019 Print ISBN-13: 9780190943943
Published to Oxford Scholarship Online: February 2021 DOI:10.1093/oso/9780190943943.001.0001

Authors

Affiliations are at time of print publication.

David McDowall, author
Distinguished Teaching Professor, School of Criminal Justice, University at Albany, State University of New York

Richard McCleary, author
Professor of Criminology, Law and Society and Planning, Policy and Design, University of California Irvine

Bradley J. Bartos, author
Ph.D. Candidate, School of Social Ecology at the University of California, Irvine