SAS/Ets User's Guide 179. 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 | 1772 F Chapter 27 The SYSLIN Procedure Figure continued Variable DF Model SUPPLY Dependent Variable q Label Quantity Parameter Estimates Parameter Standard Estimate Error t Value Pr t Variable Label Intercept 1 Intercept p 1 .0001 Price u 1 .0001 Unit Cost This output first prints the system weighted mean squared error and system weighted R2 statistics. The system weighted MSE and system weighted R2 measure the fit of the joint model obtained by stacking all the models together and performing a single regression with the stacked observations weighted by the inverse of the model error variances. See the section The R-Square Statistics on page 1799 for details. Next the table of 3SLS parameter estimates for each model is printed. This output has the same form as for the other estimation methods. Note that in some cases the 3SLS and 2SLS results can be the same. Such a case could arise because of the same principle that causes OLS and SUR results to be identical unless an equation includes a regressor not used in the other equations of the system. However the application of this principle is more complex when instrumental variables are used. When all the exogenous variables are used as instruments linear combinations of all the exogenous variables appear in the third-stage regressions through substitution of first-stage predicted values. In this example 3SLS produces different and it is hoped more efficient estimates for the demand equation. However the 3SLS and 2SLS results for the supply equation are the same. This is because the supply equation has one endogenous regressor and one exogenous regressor not used in other equations. In contrast the demand equation has fewer endogenous regressors than exogenous regressors not used in other equations in the system. Full Information Maximum Likelihood The FIML option in the PROC SYSLIN statement specifies the full information maximum .
