SAS/Ets User's Guide 5. 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 | 32 F Chapter 2 Introduction diagnostic statistics to help judge the adequacy of the model including the following - Akaike s information criterion AIC - Schwarz s Bayesian criterion SBC or BIC - Box-Ljung chi-square test statistics for white-noise residuals - autocorrelation function of residuals - partial autocorrelation function of residuals - inverse autocorrelation function of residuals - automatic outlier detection Vector Time Series Analysis The VARMAX procedure enables you to model the dynamic relationship both between the dependent variables and between the dependent and independent variables. The VARMAX procedure includes the following features several modeling features - vector autoregressive model - vector autoregressive model with exogenous variables - vector autoregressive and moving-average model - Bayesian vector autoregressive model - vector error correction model - Bayesian vector error correction model - GARCH-type multivariate conditional heteroscedasticity models criteria for automatically determining AR and MA orders - Akaike information criterion AIC - corrected AIC AICC - Hannan-Quinn HQ criterion - final prediction error FPE - Schwarz Bayesian criterion SBC also known as Bayesian information criterion BIC AR order identification aids - partial cross-correlations - Yule-Walker estimates - partial autoregressive coefficients - partial canonical correlations Vector Time Series Analysis F 33 testing the presence of unit roots and cointegration - Dickey-Fuller tests - Johansen cointegration test for nonstationary vector processes of integrated order one - Stock-Watson common trends test for the possibility of cointegration among nonstation-ary vector processes of integrated order one - Johansen cointegration test for nonstationary vector processes of integrated order two model parameter estimation methods - least squares LS - maximum likelihood ML model checks and residual analysis using the following tests - Durbin-Watson DW statistics - F test
