The SYSLIN Procedure
- Overview
-
Getting Started
An Example Model Variables in a System of Equations Using PROC SYSLIN OLS Estimation Two-Stage Least Squares Estimation LIML, K-Class, and MELO Estimation SUR, 3SLS, and FIML Estimation Computing Reduced Form Estimates Restricting Parameter Estimates Testing Parameters Saving Residuals and Predicted Values Plotting Residuals -
Syntax
-
Details
-
Examples
- References
| ANOVA Table for Instrumental Variables Methods |
In the instrumental variables methods (2SLS, LIML, K-class, MELO), first-stage predicted values are substituted for the endogenous regressors. As a result, the regression sum of squares (RSS) and the error sum of squares (ESS) do not sum to the total corrected sum of squares for the dependent variable (TSS). The analysis-of-variance table included in the second-stage results gives these sums of squares and the mean squares that are used for the 
test, but this table is not a variance decomposition in the usual sense.
The test shown in the instrumental variables case is a valid test of the no-regression hypothesis that the true coefficients of all regressors are 0. However, because of the first-stage projection of the regression mean square, this is a Wald-type test statistic, which is asymptotically but not exactly -distributed in finite samples. Thus, for small samples the test is only approximate when instrumental variables are used.