The SYSLIN Procedure
- Overview
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Getting Started
An Example ModelVariables in a System of EquationsUsing PROC SYSLINOLS EstimationTwo-Stage Least Squares EstimationLIML, K-Class, and MELO EstimationSUR, 3SLS, and FIML EstimationComputing Reduced Form EstimatesRestricting Parameter EstimatesTesting ParametersSaving Residuals and Predicted ValuesPlotting Residuals -
Syntax
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Details
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Examples
- References
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.