TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 271

SAS/Ets User's Guide 271. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 2692 F Chapter 41 Specifying Forecasting Models Figure Transformation Options You can specify a logarithmic logistic square root or Box-Cox transformation. For this example select Square Root from the list. The Transformation field is now set to Square Root. This means that the system will first take the square roots of the series values apply the additive version of the Winters method to the square root series and then produce the predictions for the original series by squaring the Winters method predictions and multiplying by a variance factor if the Mean Prediction option is set in the Forecast Options window . See Chapter 46 Forecasting Process Details for more information about predictions from transformed models. The Smoothing Model Specification window should now appear as shown in Figure . Select the OK button to fit the model. The model is added to the table of fitted models in the Develop Models window. ARIMA Model Specification Window F 2693 Figure Winter s Method Applied to Square Root Series ARIMA Model Specification Window To fit ARIMA or Box-Jenkins models not already provided in the Models to Fit window select the ARIMA model item from the pop-up menu toolbar or Edit menu. This opens the ARIMA Model Specification window as shown in Figure . 2694 F Chapter 41 Specifying Forecasting Models Figure ARIMA Model Specification Window This ARIMA Model Specification window is structured according to the Box and Jenkins approach to time series modeling. You can specify the same time series models with the Custom Model Specification window and the ARIMA Model Specification window but the windows are structured differently and you may find one more convenient than the other. At the top of the ARIMA Model Specification window is the name and label of the series and the label of the model you are specifying. The model label is filled in with an automatically generated label as you specify options. You can type over the automatic label

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