SAS/Ets User's Guide 30. 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 | 282 F Chapter 7 The ARIMA Procedure Output Correlation Analysis from the First IDENTIFY Statement The second IDENTIFY statement differences the series. The results of the second IDENTIFY statement are shown in Output . This output shows autocorrelation inverse autocorrelation and partial autocorrelation functions typical of MA 1 processes. Example Simulated IMA Model F 283 Output Correlation Analysis from the Second IDENTIFY Statement The ESTIMATE statement fits an ARIMA 0 1 1 model to the simulated data. Note that in this case the parameter estimates are reasonably close to the values used to generate the simulated data. 0 O 01 61 a2 1 a2 . Moreover the graphical analysis of the residuals shows no model inadequacies see Output and Output . The ESTIMATE statement results are shown in Output . Output Output from Fitting ARIMA 0 1 1 Model Conditional Least Squares Estimation Standard Approx Parameter Estimate Error t Value Pr t Lag MU 0 MA1 1 .0001 1 284 F Chapter 7 The ARIMA Procedure Output continued Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 99 Model for variable u Estimated Mean Period s of Differencing 1 Moving Average Factors Factor 1 1 - B 1 Output Residual Correlation Analysis of the ARIMA 0 1 1 .
