TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 291

SAS/Ets User's Guide 291. 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 | 2892 F Chapter 46 Forecasting Process Details parameter estimates. The predictions are inverse transformed median or mean and adjustments are removed. The prediction errors the difference of the dependent series and the predictions are used to compute the statistics of fit which are described in the section Series Diagnostic Tests on page 2915. The results generated by the evaluation process are displayed in the Statistics of Fit table of the Model Viewer window. Forecasting The forecasting generation process is described graphically in Figure . Forecasting F 2893 Figure Forecasting Flow Diagram The forecasting process is similar to the model evaluation process described in the preceding section except that -step-ahead predictions are made from the end of the data through the specified forecast horizon and prediction standard errors and confidence limits are calculated. The forecasts and confidence limits are displayed in the Forecast plot or table of the Model Viewer window. 2894 F Chapter 46 Forecasting Process Details Forecast Combination Models This section discusses the computation of predicted values and confidence limits for forecast combination models. See Chapter 41 Specifying Forecasting Models for information about how to specify forecast combination models and their combining weights. Given the response time series yt 1 i ng with previously generated forecasts for the m component models a combined forecast is created from the component forecasts as follows yt i Wi yi t Predictions Prediction Errors et yt yt where y i t are the forecasts of the component models and Wi are the combining weights. The estimate of the root mean square prediction error and forecast confidence limits for the combined forecast are computed by assuming independence of the prediction errors of the component forecasts as follows Standard Errors Confidence Limits ôt yjpm i w2êyt 1 f ât Za 2 where fi t are the estimated root mean square prediction errors for the .

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