The MODEL Procedure

## Example 18.16 Simulated Method of Moments—AR(1) Process

This example illustrates how to use SMM to estimate an AR(1) regression model for the following process:

In the following SAS statements, is simulated by using this model, and the endogenous variable is set to be equal to . The MOMENT statement creates two more moments for the estimation. One is the second moment and the other is the first-order autocovariance. The NPREOBS=20 option instructs PROC MODEL to run the simulation 20 times before is compared to the first observation of . Because the initial is zero, the first is . Without the NPREOBS option, this is matched with the first observation of . With NPREOBS, this , along with the next 19 , is thrown away, and the moment match starts with the twenty-first with the first observation of . This way, the initial values do not exert a large inference on the simulated endogenous variables.

```   %let nobs=500;
data ardata;
lu =0;
do i=-10 to &nobs;
x = rannor( 1011 );
e = rannor( 1011 );
u = .6 * lu + 1.5 * e;
Y = 2 + 1.5 * x + u;
lu = u;
if i > 0 then output;
end;
run;

title1 'Simulated Method of Moments for AR(1) Process';

proc model data=ardata ;
parms a b s 1 alpha .5;
instrument x;

u = alpha * zlag(u) + s * rannor( 8003 );
ysim = a + b * x + u;
y = ysim;
eq.ysq = y*y - ysim*ysim;
eq.ylagy = y * lag(y) - ysim * lag( ysim );

fit y ysq ylagy / gmm npreobs=10 ndraw=10;
bound s > 0, 1 > alpha > 0;
run;
```

The output of the MODEL procedure is shown in Output 18.16.1:

Output 18.16.1 PROC MODEL Output
 Simulated Method of Moments for AR(1) Process

The MODEL Procedure

Model Summary
Model Variables 1
Parameters 4
Equations 3
Number of Statements 6
Program Lag Length 1

Model Variables Y a b s(1) alpha(0.5) ysq ylagy Y

The 3 Equations to Estimate
Y = F(a(1), b(x), s, alpha)
ysq = F(a, b, s, alpha)
ylagy = F(a, b, s, alpha)
Instruments 1 x

Nonlinear GMM Parameter Estimates
Parameter Estimate Approx Std Err t Value Approx
Pr > |t|
a 1.632798 0.1038 15.73 <.0001
b 1.513197 0.0698 21.67 <.0001
s 1.427888 0.0984 14.52 <.0001
alpha 0.543985 0.0809 6.72 <.0001

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