The SYSLIN procedure estimates parameters in an interdependent system of linear regression equations.
Ordinary least squares (OLS) estimates are biased and inconsistent when current period endogenous variables appear as regressors in other equations in the system. The errors of a set of related regression equations are often correlated, and the efficiency of the estimates can be improved by taking these correlations into account. The SYSLIN procedure provides several techniques that produce consistent and asymptotically efficient estimates for systems of regression equations.
The SYSLIN procedure provides the following estimation methods:
ordinary least squares (OLS)
two-stage least squares (2SLS)
limited information maximum likelihood (LIML)
K-class
seemingly unrelated regressions (SUR)
iterated seemingly unrelated regressions (ITSUR)
three-stage least squares (3SLS)
iterated three-stage least squares (IT3SLS)
full information maximum likelihood (FIML)
minimum expected loss (MELO)
Other features of the SYSLIN procedure enable you to:
impose linear restrictions on the parameter estimates
test linear hypotheses about the parameters
write predicted and residual values to an output SAS data set
write parameter estimates to an output SAS data set
write the crossproducts matrix (SSCP) to an output SAS data set
use raw data, correlations, covariances, or cross products as input