The SYSLIN procedure provides regression analysis of a simultaneous system of linear equations with a a full range of estimation methods, including
ordinary least squares (OLS)
two-stage least squares (2SLS)
three-stage least squares (3SLS)
iterated 3SLS
seemingly unrelated regression (SUR)
iterated SUR
limited-information maximum-likelihood (LIML)
full-information maximum-likelihood (FIML)
minimum-expected-loss (MELO)
general K-class estimators
In addition, PROC SYSLIN offers weighted regression and restrictions for any linear combination of coefficients, within a single equation model or across models. Tests are available for any linear hypothesis, for the parameters of a single model or across equations.
The SYSLIN procedure provides a wide range of model diagnostics and statistics, including
usual ANOVA tables and R² statistics
Durbin-Watson statistics
standardized coefficients
test for over-identifying restrictions
residual plots
standard errors and t tests
covariance and correlation matrices of parameter estimates and equation errors
Predicted values, residuals, parameter estimates, and variance-covariance matrices can be saved in SAS data sets.
For further details, see the SAS/ETS^{®} User's Guide