{"title":"Into the Future","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/oso/9780190943943.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780190943943.003.0006","url":null,"abstract":"\u0000 Chapter 6 introduces two conceptual issues that, in our opinion, will become important in the near future. The first involves the validity of statistical inference. Critics of the conventional null hypothesis significance test generally focus on the undue influence of sample size on p-values and the common misinterpretation of significance levels. Bayesian approaches address and, to some extent, solve both shortcomings. The second conceptual issue involves the use of control time series. As a rule, valid causal inferences require the use of a contrasting control time series. In most instances, no ideal control series is available; however, a synthetic ideal control series can sometimes be constructed from an ensemble of less-than-ideal control time series.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134550854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Noise Component:N(at)","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/oso/9780190943943.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780190943943.003.0003","url":null,"abstract":"\u0000 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.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Auxiliary Modeling Procedures","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/oso/9780190943943.003.0005","DOIUrl":"https://doi.org/10.1093/oso/9780190943943.003.0005","url":null,"abstract":"\u0000 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.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121571995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to ITSA","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/OSO/9780190943943.003.0001","DOIUrl":"https://doi.org/10.1093/OSO/9780190943943.003.0001","url":null,"abstract":"\u0000 Chapter 1 introduces Interrupted Time Series Analysis (ITSA) as a toolbox for researchers whose data consist of a long sequence of observations | say, N ≥15 observations | measured before and after a treatment or intervention. Sometimes the treatment or intervention is implemented by the researcher, other times it occurs naturally or by accident. The chapter also describes a family of impact types, characterized by their onset (abrupt or gradual) and duration (permanent or temporary); and the essential role of counterfactual controls in causal inference. The chapter concludes with an outline and summary of the book's subsequent chapters.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129606061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ARIMA Algebra","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/oso/9780190943943.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780190943943.003.0002","url":null,"abstract":"\u0000 Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies that the time series process operated identically in the past as it does in the present and that it will continue to operate identically in the future. Without stationarity, the properties of the time series would vary with the time frame and no inferences about the underlying process would be possible. A seasonally nonstationary process drifts or trends in annual steps. The “best” seasonal model structure is the one that transforms the series to white noise with the fewest number of parameters.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116080953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Intervention Component: X( It)","authors":"David McDowall, R. McCleary, Bradley J. Bartos","doi":"10.1093/oso/9780190943943.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780190943943.003.0004","url":null,"abstract":"\u0000 Chapter 4 introduces the full ARIMA intervention model. Most substantive theories specify the intervention as an exogenous dichotomy. A Box-Tiao transfer function then distributes the intervention's response across the endogenous time series to reflect a theoretically specified onset and duration. Transfer functions allow the noise component to be parsed from the residualized time series. Theoretical specification of the intervention model requires at least some sense of the onset and duration of the impact. Detailed analyses of ten time series demonstrate how to handle interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts. One popular version of an ITSA short course ends with Chapter 4. Although statistically adequate ARIMA models can be built using the modeling strategy described in Chapters 3-4, survey knowledge of the auxiliary methods described in Chapter 5 is recommended.","PeriodicalId":180500,"journal":{"name":"Interrupted Time Series Analysis","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126997532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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