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Design and Analysis of Time Series Experiments | Oxford Scholarship Online
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Design and Analysis of Time Series Experiments

Richard McCleary, David McDowall, and Bradley Bartos

Abstract

Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, 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. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell ... More

Keywords: ARIMA models, Box-Jenkins-Tiao strategy, statistical conclusion validity, internal validity, construct validity, external validity, four-validity system, Rubin causal model, counterfactual identification, synthetic control time designs

Bibliographic Information

Print publication date: 2017 Print ISBN-13: 9780190661557
Published to Oxford Scholarship Online: May 2017 DOI:10.1093/oso/9780190661557.001.0001

Authors

Affiliations are at time of print publication.

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

David McDowall, author
Distinguished Teaching Professor, School of Criminal Justice, University at Albany, SUNY

Bradley Bartos, author
Graduate Student, School of Social Ecology, University of California, Irvine