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SAS/Ets 9.22 User's Guide 43. 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 | 412 F Chapter 8 The AUTOREG Procedure Printed Output The AUTOREG procedure prints the following items 1. the name of the dependent variable 2. the ordinary least squares estimates 3. Estimates of autocorrelations which include the estimates of the autocovariances the autocorrelations and if there is sufficient space a graph of the autocorrelation at each LAG 4. if the PARTIAL option is specified the partial autocorrelations 5. the preliminary MSE which results from solving the Yule-Walker equations. This is an estimate of the final MSE. 6. the estimates of the autoregressive parameters Coefficient their standard errors Standard Error and the ratio of estimate to standard error t Value 7. the statistics of fit for the final model. These include the error sum of squares SSE the degrees of freedom for error DFE the mean square error MSE the mean absolute error MAE the mean absolute percentage error MAPE the root mean square error Root MSE the Schwarz information criterion SBC the Hannan-Quinn information criterion HQC the Akaike information criterion AIC the corrected Akaike information criterion AICC the Durbin-Watson statistic Durbin-Watson the regression R2 Regress R-square and the total R2 Total R-square . For GARCH models the following additional items are printed the value of the log-likelihood function Log Likelihood the number of observations that are used in estimation Observations the unconditional variance Uncond Var the normality test statistic and its p-value Normality Test and Pr ChiSq 8. the parameter estimates for the structural model Estimate a standard error estimate Standard Error the ratio of estimate to standard error t Value and an approximation to the significance probability for the parameter being 0 Approx Pr Itl 9. If the NLAG option is specified with METHOD ULS or METHOD ML the regression parameter estimates are printed again assuming that the autoregressive parameter estimates are known. In this case the Standard Error and related .
