### Output Data Sets

#### OUT= Data Set

For normalized form equations, the OUT= data set specified in the FIT statement contains residuals, actuals, and predicted values of the dependent variables computed from the parameter estimates. For general form equations, actual values of the endogenous variables are copied for the residual and predicted values.

The variables in the data set are as follows:

• BY variables

• RANGE variable

• ID variables

• _ESTYPE_, a character variable of length 8 that identifies the estimation method: OLS, SUR, N2SLS, N3SLS, ITOLS, ITSUR, IT2SLS, IT3SLS, GMM, ITGMM, or FIML

• _TYPE_, a character variable of length 8 that identifies the type of observation: RESIDUAL, PREDICT, or ACTUAL

• _WEIGHT_, the weight of the observation in the estimation. The _WEIGHT_ value is 0 if the observation was not used. It is equal to the product of the _WEIGHT_ model program variable and the variable named in the WEIGHT statement, if any, or 1 if weights were not used.

• the WEIGHT statement variable if used

• the model variables. The dependent variables for the normalized form equations in the estimation contain residuals, actuals, or predicted values, depending on the _TYPE_ variable, whereas the model variables that are not associated with estimated equations always contain actual values from the input data set.

• any other variables named in the OUTVARS statement. These can be program variables computed by the model program, CONTROL variables, parameters, or special variables in the model program.

The following SAS statements are used to generate and print an OUT= data set:

```proc model data=gmm2;
exogenous x1 x2;
parms a1 a2 b2 b1 2.5 c2 55 d1;
inst b1 b2 c2 x1 x2;
y1 = a1 * y2 + b1 * x1 * x1 + d1;
y2 = a2 * y1 + b2 * x2 * x2 + c2 / x2 + d1;

fit y1 y2 / 3sls gmm out=resid outall ;
run;
```
```proc print data=resid(obs=20);
run;
```

The data set GMM2 was generated by the example in the preceding ESTDATA= section above. A partial listing of the RESID data set is shown in Figure 19.69.

Figure 19.69: The OUT= Data Set

Obs _ESTYPE_ _TYPE_ _WEIGHT_ x1 x2 y1 y2
1 3SLS ACTUAL 1 1.00000 -1.7339 -3.05812 -23.071
2 3SLS PREDICT 1 1.00000 -1.7339 -0.36806 -19.351
3 3SLS RESIDUAL 1 1.00000 -1.7339 -2.69006 -3.720
4 3SLS ACTUAL 1 1.41421 -5.3046 0.59405 43.866
5 3SLS PREDICT 1 1.41421 -5.3046 -0.49148 45.588
6 3SLS RESIDUAL 1 1.41421 -5.3046 1.08553 -1.722
7 3SLS ACTUAL 1 1.73205 -5.2826 3.17651 51.563
8 3SLS PREDICT 1 1.73205 -5.2826 -0.48281 41.857
9 3SLS RESIDUAL 1 1.73205 -5.2826 3.65933 9.707
10 3SLS ACTUAL 1 2.00000 -0.6878 3.66208 -70.011
11 3SLS PREDICT 1 2.00000 -0.6878 -0.18592 -76.502
12 3SLS RESIDUAL 1 2.00000 -0.6878 3.84800 6.491
13 3SLS ACTUAL 1 2.23607 -7.0797 0.29210 99.177
14 3SLS PREDICT 1 2.23607 -7.0797 -0.53732 92.201
15 3SLS RESIDUAL 1 2.23607 -7.0797 0.82942 6.976
16 3SLS ACTUAL 1 2.44949 14.5284 1.86898 423.634
17 3SLS PREDICT 1 2.44949 14.5284 -1.23490 421.969
18 3SLS RESIDUAL 1 2.44949 14.5284 3.10388 1.665
19 3SLS ACTUAL 1 2.64575 -0.6968 -1.03003 -72.214
20 3SLS PREDICT 1 2.64575 -0.6968 -0.10353 -69.680

#### OUTEST= Data Set

The OUTEST= data set contains parameter estimates and, if requested, estimates of the covariance of the parameter estimates.

The variables in the data set are as follows:

• BY variables

• _NAME_, a character variable of length 32, blank for observations that contain parameter estimates or a parameter name for observations that contain covariances

• _TYPE_, a character variable of length 8 that identifies the estimation method: OLS, SUR, N2SLS, N3SLS, ITOLS, ITSUR, IT2SLS, IT3SLS, GMM, ITGMM, or FIML

• _STATUS_, variable that gives the convergence status of estimation. _STATUS_ = 0 when convergence criteria are met, = 1 when estimation converges with a note, = 2 when estimation converges with a warning, and = 3 when estimation fails to converge

• _NUSED_, the number of observations used in estimation

• the parameters estimated

If the COVOUT option is specified, an additional observation is written for each row of the estimate of the covariance matrix of parameter estimates, with the _NAME_ values that contain the parameter names for the rows. Parameter names longer than 32 characters are truncated.

#### OUTPARMS= Data Set

The option OUTPARMS= writes all the parameter estimates to an output data set. This output data set contains one observation and is similar to the OUTEST= data set, but it contains all the parameters, is not associated with any FIT task, and contains no covariances. The OUTPARMS= option is used in the PROC MODEL statement, and the data set is written at the end, after any FIT or SOLVE steps have been performed.

#### OUTS= Data Set

The OUTS= SAS data set contains the estimate of the covariance matrix of the residuals across equations. This matrix is formed from the residuals that are computed by using the parameter estimates.

The variables in the OUTS= data set are as follows:

• BY variables

• _NAME_, a character variable that contains the name of the equation

• _TYPE_, a character variable of length 8 that identifies the estimation method: OLS, SUR, N2SLS, N3SLS, ITOLS, ITSUR, IT2SLS, IT3SLS, GMM, ITGMM, or FIML

• variables with the names of the equations in the estimation

Each observation contains a row of the covariance matrix. The data set is suitable for use with the SDATA= option in a subsequent FIT or SOLVE statement. (See the section Tests on Parameters in this chapter for an example of the SDATA= option.)

#### OUTSUSED= Data Set

The OUTSUSED= SAS data set contains the covariance matrix of the residuals across equations that is used to define the objective function. The form of the OUTSUSED= data set is the same as that for the OUTS= data set.

Note that OUTSUSED= is the same as OUTS= for the estimation methods that iterate the S matrix (ITOLS, IT2SLS, ITSUR, and IT3SLS). If the SDATA= option is specified in the FIT statement, OUTSUSED= is the same as the SDATA= matrix read in for the methods that do not iterate the S matrix (OLS, SUR, N2SLS, and N3SLS).

#### OUTV= Data Set

The OUTV= data set contains the estimate of the variance matrix, . This matrix is formed from the instruments and the residuals that are computed by using the final parameter estimates obtained from the estimation method chosen.

An estimate of obtained from 2SLS is used in GMM estimation. Hence if you input the dataset obtained from the OUTV statement in 2SLS into the VDATA statement while fitting GMM, you get the same result by fitting GMM directly without specifying the VDATA option.