Introduction

Simultaneous Systems Linear Regression

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