Simultaneous Systems Linear Regression
The SYSLIN procedure provides regression analysis of a simultaneous
system of linear equations.
The SYSLIN procedure includes the following features:
- estimation of parameters in simultaneous
systems of linear equations
- 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
- weighted regression
- any number of restrictions for any linear combination of coefficients,
within a single model or across equations
- tests for any linear hypothesis, for the parameters of
a single model or across equations
- 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 saved in output SAS data sets
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
