The SYSLIN and ENTROPY procedures provide 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 the following:

  • ordinary least squares (OLS)

  • two-stage least squares (2SLS)

  • three-stage least squares (3SLS)

  • iterated 3SLS (IT3SLS)

  • seemingly unrelated regression (SUR)

  • iterated SUR (ITSUR)

  • 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 the following:

  • usual ANOVA tables and R-square statistics

  • Durbin-Watson statistics

  • standardized coefficients

  • test for overidentifying 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

• other features of the SYSLIN procedure that enable you to do the following:

  • 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

The ENTROPY procedure supports the following models and features:

• generalized maximum entropy (GME) estimation

• generalized cross entropy (GCE) estimation

• normed moment generalized maximum entropy

• maximum entropy-based seemingly unrelated regression (MESUR) estimation

• pure inverse estimation

• estimation of parameters in simultaneous systems of linear equations

• Markov models

• unordered multinomial choice problems

• 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