TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 273

SAS/Ets User's Guide 273. 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 | 2712 F Chapter 41 Specifying Forecasting Models If no models are selected the Fit Regression Weights button fits weights for all the models in the list. You can compute regression weights for only some of the models by first selecting the models you want to combine and then selecting Fit Regression Weights. In this case only the nonmissing Weight values are replaced with regression weights. As an example of how to combine forecasting models select all the models in the list. After you have finished selecting the models all the models in the list should now have equal weight values which implies a simple average of the forecasts. Now select the Fit Regression Weights button. The system performs a linear regression of the series on the predictions from the models with nonmissing weight values and replaces the weight values with the estimated regression coefficients. These are the combining weights that produce the smallest mean square prediction error within the sample. The Forecast Combination window should now appear as shown in Figure . Note that some of the regression weight values are negative. Figure Combining Models Select the OK button to fit the combined model. Now the Develop Models window shows this model to be the best fitting according to the root mean square error as shown in Figure . Incorporating Forecasts from Other Sources F 2713 Figure Develop Models Window Showing All Models Fit Notice that the combined model has a smaller root mean square error than any one of the models included in the combination. The confidence limits for forecast combinations are produced by taking a weighted average of the mean square prediction errors for the component forecasts ignoring the covariance between the prediction errors. Incorporating Forecasts from Other Sources You might have forecasts from other sources that you want to include in the forecasting process. Examples of other forecasts you might want to use are best guess forecasts based on

• Tài chính doanh nghiệp
• Hướng dẫn sử dụng SAS ETS
• các mô hình phân tích tài chính
• báo cáo tài chính
• phân tích đầu tư
• phân tích tính khả dụng các khoản đầu tư