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*testscovariance 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

Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.