SAS/Ets User's Guide 45. 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 | 432 F Chapter 8 The AUTOREG Procedure Output continued Expected Autocorrelations Lag Autocorr 0 1 2 3 4 5 Autoregressive parameters assumed given Variable DF Standard Approx Pr t Estimate Error t Value Intercept 1 Output Diagnostic Plots Example Missing Values F 433 The following statements plot the residuals and confidence limits data reshape1 set a miss . if r . then do miss p p . end run title Predicted Values and Confidence Limits proc sgplot data reshape1 NOAUTOLEGEND band x i upper u lower l scatter y miss x i MARKERATTRS symbol x color red series y p x i markers MARKERATTRS color blue lineattrs color blue run The plot of the predicted values and the upper and lower confidence limits is shown in Output . Note that the confidence interval is wider at the beginning of the series when there are no past noise values to use in the forecast equation and after missing values where again there is an incomplete set of past residuals. 434 F Chapter 8 The AUTOREG Procedure Output Plot of Predicted Values and Confidence Interval Example Money Demand Model This example estimates the log-log money demand equation by using the maximum likelihood method. The money demand model contains four explanatory variables. The lagged nominal money stock M1 is divided by the current price level GDF to calculate a new variable M1CP since the money stock is assumed to follow the partial adjustment process. The variable M1CP is then used to estimate the coefficient of adjustment. All variables are transformed using the natural logarithm with a DATA step. Refer to Balke and Gordon 1986 for a data description. The first eight observations are printed using the PRINT procedure and are shown in Output . Note that the first observation of the variables M1CP and INFR are missing. Therefore the money demand equation is estimated for the period 1968 2 to 1983 4 since PROC AUTOREG ignores the
