Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
SAS/Ets 9.22 User's Guide 207. 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 | 2052 F Chapter 32 The VARMAX Procedure Figure 32.1 Plot of Generated Data Process The following statements fit a VAR 1 model to the simulated data. First you specify the input data set in the PROC VARMAX statement. Then you use the MODEL statement to designate the dependent variables y1 and y2. To estimate a VAR model with mean zero you specify the order of the autoregressive model with the P option and the NOINT option. The MODEL statement fits the model to the data and prints parameter estimates and their significance. The PRINT ESTIMATES option prints the matrix form of parameter estimates and the PRINT DIAGNOSE option prints various diagnostic tests. The LAGMAX 3 option is used to print the output for the residual diagnostic checks. To output the forecasts to a data set you specify the OUTPUT statement with the OUT option. If you want to forecast five steps ahead you use the LEAD 5 option. The ID statement specifies the yearly interval between observations and provides the Time column in the forecast output. Vector Autoregressive Process F 2053 The VARMAX procedure output is shown in Figure 32.2 through Figure 32.10. --- Vector Autoregressive Model ----- proc varmax data simul1 id date interval year model y1 y2 p 1 noint lagmax 3 print estimates diagnose output out for lead 5 run Figure 32.2 Descriptive Statistics The VARMAX Procedure Number of Observations 100 Number of Pairwise Missing 0 Simple Summary Statistics Standard Variable Type N Mean Deviation Min Max yi Dependent 100 -0.21653 2.78210 -4.75826 8.37032 y2 Dependent 100 0.16905 2.58184 -6.04718 9.58487 The VARMAX procedure first displays descriptive statistics. The Type column specifies that the variables are dependent variables. The column N stands for the number of nonmissing observations. Figure 32.3 shows the type and the estimation method of the fitted model for the simulated data. It also shows the AR coefficient matrix in terms of lag 1 the parameter estimates and their significance which can .
