tailieunhanh - SAS/ETS 9.22 User's Guide 222

SAS/Ets User's Guide 222. 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 | 2202 F Chapter 32 The VARMAX Procedure Output shows the innovation covariance matrix estimates the various information criteria results and the tests for white noise residuals. The residuals have significant correlations at lag 2 and 3. The Portmanteau test results into significant. These results show that a VECM 3 model might be better fit than the VECM 2 model is. Output Diagnostic Checks Covariances of Innovations Variable yi y2 y3 y4 yi y2 y3 y4 Information Criteria AICC HQC AIC SBC FPEC Schematic Representation of Cross Correlations of Residuals Variable Lag 0 1 2 3 4 5 6 y1 . . . .-- . y2 . y3 . .-. . -. . y4 . . . . is 2 std error - is -2 std error . is between Portmanteau Test for Cross Correlations of Residuals Up To Lag DF Chi-Square Pr ChiSq 3 16 .0001 4 32 .0001 5 48 .0001 6 64 .0001 Example Analysis of . Economic Variables F 2203 Output describes how well each univariate equation fits the data. The residuals for y3 and y4 are off from the normality. Except the residuals for y3 there are no AR effects on other residuals. Except the residuals for y4 there are no ARCH effects on other residuals. Output Diagnostic Checks Continued Univariate Model ANOVA Diagnostics Standard Variable R-Square Deviation F Value Pr F yi .0001 y2 .0001 y3 y4 Univariate Model White Noise Diagnostics Variable Durbin Watson Normality ARCH Chi-Square Pr ChiSq F Value Pr F y1 y2 y3 .0001 y4 .0001 .0001 Univariate Model AR Diagnostics Variable AR1 AR2 AR3 AR4 F Value Pr F F Value Pr F F Value Pr F F Value Pr F y1