Providing software solutions since 1976
The SYSLIN Procedure
The SYSLIN procedure provides regression analysis of a simultaneous system of linear equations with a a full range of estimation methods, including
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ordinary least squares (OLS)
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two-stage least squares (2SLS)
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three-stage least squares (3SLS)
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iterated 3SLS
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seemingly unrelated regression (SUR)
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iterated SUR
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limited-information maximum-likelihood (LIML)
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full-information maximum-likelihood (FIML)
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minimum-expected-loss (MELO)
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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
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usual ANOVA tables and R² statistics
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Durbin-Watson statistics
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standardized coefficients
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test for over-identifying restrictions
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residual plots
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standard errors and t tests
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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: The SYSLIN Procedure.
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Examples
- Klein's Model I Estimated with LIML and 3SLS
- Grunfeld's Model Estimated with SUR
- Illustration of ODS Graphics
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