tailieunhanh - SAS/ETS 9.22 User's Guide 177

SAS/Ets User's Guide 177. 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 | 1752 F Chapter 26 The STATESPACE Procedure 7. a schematic representation of the partial autocorrelation matrices showing the significant partial autocorrelations 8. the Yule-Walker estimates of the autoregressive parameters for the autoregressive model with the minimum AIC 9. if the PRINTOUT LONG option is specified the autocovariance matrices of the residuals of the minimum AIC model. This is the sequence of estimated innovation variance matrices for the solutions of the Yule-Walker equations. 10. if the PRINTOUT LONG option is specified the autocorrelation matrices of the residuals of the minimum AIC model 11. If the CANCORR option is specified the canonical correlations analysis for each potential state vector considered in the state vector selection process. This includes the potential state vector the canonical correlations the information criterion for the smallest canonical correlation Bartlett s 2 statistic Chi Square for the smallest canonical correlation and the degrees of freedom of Bartlett s 2. 12. the components of the chosen state vector 13. the preliminary estimate of the transition matrix F the input matrix G and the variance matrix for the innovations Xee 14. if the ITPRINT option is specified the iteration history of the likelihood maximization. For each iteration this shows the iteration number the number of step halvings the determinant of the innovation variance matrix the damping factor Lambda and the values of the parameters. 15. the state vector printed again to aid interpretation of the following listing of F and G 16. the final estimate of the transition matrix F 17. the final estimate of the input matrix G 18. the final estimate of the variance matrix for the innovations Xee 19. a table that lists the estimates of the free parameters in F and G and their standard errors and t statistics 20. if the COVB option is specified the covariance matrix of the parameter estimates 21. if the COVB option is specified the correlation matrix of the .