Performing interrupted time series analyses using SPSS
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Hello
I would be most grateful for advice on functionality within each of versions 22 and 24 of SPSS to enable me to perform interrupted time series analyses. On searching online, I can see old posts referring to ARIMA. However, I am not clear about progress in functionality
for interrupted time series analyses using the above versions of SPSS. Is a particular add-on module needed or is all of the functionality already built in? Are there up-to-date resources
available to assist me?
Thanks in advance! Best wishes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dr Margaret MacDougall (Senior Lecturer) Centre for Population Health Sciences University of Edinburgh Medical Shool Teviot Place Edinburgh EH8 9AG Tel: +44(0)131 650 3211 Fax: +44(0)131 650 6909 Email:
[hidden email] https://www.ed.ac.uk/profile/margaret-macdougall
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The TSMODEL procedure allows for intervention variables and is included in the Forecasting option (which may be part of a larger bundle such as Professional). It appears under Analyze > Forecasting if it is installed. It has been around for some time. There is a newer procedure, TCM MODEL with related commands TCM ANALYSIS and TCM APPLY, for temporal causal models also in the Forecasting option that you might find of interest. Here is the introductory paragraph from the CSR. The TCM MODEL procedure builds temporal causal models. In temporal causal modeling, you specify a set of target series and a set of candidate inputs to those targets. The procedure then builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modeling where you must explicitly specify the predictors for a target series. Since temporal causal modeling typically involves building models for multiple related time series, the result is referred to as a model system. In the context of temporal causal modeling, the term causal refers to Granger causality. A time series X is said to "Granger cause" another time series Y if regressing for Y in terms of past values of both X and Y results in a better model for Y than regressing only on past values of Y. You can see the full documentation including the algorithms online. Here is a link to the case studies for time series. There is also an extension command STATS GARCH, which requires the R Essentials, that does a variety of GARCH models. It can be installed from the Extensions menu if it is not already there, and documentation is via the dialog box or syntax help (press F1 on an instance in the Syntax Editor). It is a no-cost add-on. On Fri, Aug 24, 2018 at 10:24 AM MACDOUGALL Margaret <[hidden email]> wrote:
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In reply to this post by margmacd
Have not used TSMODEL, but I've done models before using ARIMA, which you
just include dummy variables to do interrupted time series models. See below for an example: - Blog post, crimes in Baltimore, https://andrewpwheeler.wordpress.com/2017/09/03/graphs-and-interrupted-time-series-analysis-trends-in-major-crimes-in-baltimore/ - data and code to replicate, https://www.dropbox.com/s/4ta8p88eshydcvt/Baltimore_ARIMA.zip?dl=0 I do not know if ARIMA comes with base SPSS or you need extras though. ----- Andy W [hidden email] http://andrewpwheeler.wordpress.com/ -- Sent from: http://spssx-discussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
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