PROC MODEL currently supports two methods for minimizing the objective function. These methods are described in the following sections.
GAUSS
The Gauss-Newton parameter-change vector for a system with g equations, n nonmissing observations, and p unknown parameters is
where
is the change vector, X is the stacked
Jacobian matrix of partial derivatives of the residuals with respect to the parameters, and r is an
vector of the stacked residuals. The components of X and r are weighted by the S
matrix. When instrumental methods are used, X and r are the projections of the Jacobian matrix and residuals vector in the instruments space and not the Jacobian and residuals themselves. In the preceding formula, S and W are suppressed. If instrumental variables are used, then the change vector becomes:
|
 |
|
|
This vector is computed at the end of each iteration. The objective function is then computed at the changed parameter values at the start of the next iteration. If the objective function is not improved by the change, the
vector is reduced by one-half and the objective function is reevaluated. The change vector will be halved up to MAXSUBITER= times until the objective function is improved. If the objective function cannot be improved after MAXSUBITER= times, the procedure switches to the MARQUARDT method described in the next section to further improve the objective function.
For FIML, the
matrix is substituted with one of three choices for approximations to the Hessian. See the section Full Information Maximum Likelihood Estimation (FIML) in this chapter.