Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments.
Author(s): David McDowall; Richard McCleary; Bradley J. Bartos
Publisher: Oxford University Press, USA
Year: 2019
Language: English
Pages: xviii+182
Cover
Praise Page
Interrupted Time Series Analysis
Copyright page
Table of Contents
List of Figures
List of Tables
Acknowledgments
1. Introduction to ITSA
1.1. An Outline
1.2. A Short Note On Software
2. Arima Algebra
2.1. White Noise Processes
2.2. Ar1 Andma1 Processes
2.3. Ar And Ma “Memory”
2.4. Higher-Order Andmixed Processes
2.5. Invertibility And Stationarity
2.6. Integrated Processes
2.7. Stationarity Revisited
2.8. Seasonal Models
2.9. Conclusion
3. The Noise Component: N(At)
3.1. White Noise
3.2. The Normality Assumption: A Digression
3.3. Ar1 Andma1 Time Series
3.3.1. Canadian Inflation
3.3.2. U.K. Gdp Growth
3.3.3. Pediatric Trauma Admissions
3.4. Higher-Order Arma Processes
3.4.1. Beveridge’Swheat Price Time Series
3.4.2. Zurich Sunspot Numbers
3.5. Integrated Models
3.5.1. Kroeber'S Skirt-Width Time Series
3.5.2. Annual U.S. Tuberculosis Cases
3.6. Seasonalmodels
3.6.1. Anchoragemonthly Precipitation
3.6.2. Monthly Atmospheric
3.6.3. Australian Traffic Fatalities
3.7. Conclusion
4. The Intervention Component: X(It)
4.1. Abrupt, Permanent Impacts
4.1.1. Rest Breaks And Productivity
4.1.2. Prophylactic Vancomycin And Surgical Infection
4.1.3. New Hampshiremedicaid Prescriptions
4.1.4. Methadonemaintenance Treatments
4.2. Gradually Accruing Impacts
4.2.1. Australian Traffic Fatalities
4.2.2. British Traffic Fatalities
4.2.3. “Talking Out” Incidents
4.3. Decaying Impacts
4.3.1. Self-Injurious Behavior
4.4. Complex Impacts
4.4.1. Decriminalization Of Public Drunkenness
4.5. Conclusion
5. Auxiliarymodeling Procedures
5.1. Information Criteria
5.2. Unit Root Tests
5.3. Co-Integrated Time Series
5.4. Conclusion
6. Into The Future
6.1. Bayesian Hypothesis Testing
6.2. Synthetic Control Designs
6.3. Conclusion
References
Index
