tailieunhanh - SAS/ETS 9.22 User's Guide 223

SAS/Ets User's Guide 223. 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 | 2212 F Chapter 32 The VARMAX Procedure Output Proportions of Prediction Error Covariance Decomposition Proportions of Prediction Error Covariances by Variable Variable Lead yi y2 y3 yi i 2 3 4 5 6 y2 i 2 3 4 5 6 y3 i 2 3 4 5 6 The table in Output gives forecasts and their prediction error covariances. Output Forecasts Forecasts Standard Variable Obs Time Forecast Error 95 Confidence Limits y1 77 1979 1 78 1979 2 79 1979 3 80 1979 4 81 1980 1 y2 77 1979 1 78 1979 2 79 1979 3 80 1979 4 81 1980 1 y3 77 1979 1 78 1979 2 79 1979 3 80 1979 4 81 1980 1 Example Analysis of German Economic Variables F 2213 Output shows that you cannot reject Granger noncausality from y2 y 3 to y1 using the significance level. Output Granger Causality Tests Granger-Causality Wald Test Test DF Chi-Square Pr ChiSq 1 4 Test 1 Group 1 Variables y1 Group 2 Variables y2 y3 The following SAS statements show that the variable y1 is the exogenous variable and fit the VARX 2 1 model to the data. proc varmax data use id date interval qtr model y2 y3 y1