User manual SPSS TRENDS 14.0

[. . . ] SPSS Trends 14. 0 TM For more information about SPSS® software products, please visit our Web site at http://www. spss. com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark and the other product names are the trademarks of SPSS Inc. No material describing such software may be produced or distributed without the written permission of the owners of the trademark and license rights in the software and the copyrights in the published materials. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. [. . . ] Displays a table of residual partial autocorrelations by lag for each estimated model. The table includes the confidence intervals for the partial autocorrelations. This table is only available if model parameters are reestimated (Reestimate from data on the Models tab). Display forecasts. Displays a table of model forecasts and confidence intervals for each model. 41 Apply Time Series Models Plots Figure 3-3 Apply Time Series Models, Plots tab The Plots tab provides options for displaying plots of model fit statistics, autocorrelation functions, and series values (including forecasts). Plots for Comparing Models This group of options controls the display of plots containing statistics across all models. Unless model parameters are reestimated (Reestimate from data on the Models tab), displayed values are those from the model file and reflect the data used when each 42 Chapter 3 model was developed (or last updated). In addition, autocorrelation plots are only available if model parameters are reestimated. You can select one or more of the following options: Stationary R-square R-square Root mean square error Mean absolute percentage error Mean absolute error Maximum absolute percentage error Maximum absolute error Normalized BIC Residual autocorrelation function (ACF) Residual partial autocorrelation function (PACF) For more information, see "Goodness-of-Fit Measures" in Appendix A on p. 125. Plots for Individual Models Series. Select (check) this option to obtain plots of the predicted values for each model. Observed values, fit values, confidence intervals for fit values, and autocorrelations are only available if model parameters are reestimated (Reestimate from data on the Models tab). You can select one or more of the following for inclusion in the plot: Observed values. The observed values of the dependent series. The model predicted values for the forecast period. The model predicted values for the estimation period. The confidence intervals for the forecast period. The confidence intervals for the estimation period. Residual autocorrelation function (ACF). Displays a plot of residual autocorrelations for each estimated model. Residual partial autocorrelation function (PACF). Displays a plot of residual partial autocorrelations for each estimated model. 43 Apply Time Series Models Limiting Output to the Best- or Poorest-Fitting Models Figure 3-4 Apply Time Series Models, Output Filter tab The Output Filter tab provides options for restricting both tabular and chart output to a subset of models. You can choose to limit output to the best-fitting and/or the poorest-fitting models according to fit criteria you provide. By default, all models are included in the output. Unless model parameters are reestimated (Reestimate from data on the Models tab), values of fit measures used for filtering models are those from the model file and reflect the data used when each model was developed (or last updated). 44 Chapter 3 Best-fitting models. Select (check) this option to include the best-fitting models in the output. Select a goodness-of-fit measure and specify the number of models to include. [. . . ] A group of two or more consecutive additive outliers. Selecting this outlier type results in the detection of individual additive outliers in addition to patches of them. 127 Appendix Guide to ACF/PACF Plots C The plots shown here are those of pure or theoretical ARIMA processes. Here are some general guidelines for identifying the process: Nonstationary series have an ACF that remains significant for half a dozen or more lags, rather than quickly declining to 0. You must difference such a series until it is stationary before you can identify the process. [. . . ]