# The MODEL Procedure

### Example 19.3 Vector AR(1) Estimation

This example shows the estimation of a two-variable vector AR(1) error process for the Grunfeld model (Grunfeld and Griliches, 1960) by using the %AR macro. First, the full model is estimated. Second, the model is estimated with the restriction that the errors are univariate AR(1) instead of a vector process. The following statements produce Output 19.3.1 through Output 19.3.5.

```data grunfeld;
input year gei gef gec whi whf whc;
label gei = 'Gross Investment GE'
gec = 'Capital Stock Lagged GE'
gef = 'Value of Outstanding Shares GE Lagged'
whi = 'Gross Investment WH'
whc = 'Capital Stock Lagged WH'
whf = 'Value of Outstanding Shares Lagged WH';
datalines;
1935     33.1      1170.6    97.8      12.93     191.5     1.8
1936     45.0      2015.8    104.4     25.90     516.0     .8
1937     77.2      2803.3    118.0     35.05     729.0     7.4

... more lines ...

```
```title1 'Example of Vector AR(1) Error Process Using Grunfeld''s Model';
/* Note: GE stands for General Electric
WH stands for Westinghouse     */

proc model outmodel=grunmod;
var gei whi gef gec whf whc;
parms ge_int ge_f ge_c wh_int wh_f wh_c;
label ge_int = 'GE Intercept'
ge_f   = 'GE Lagged Share Value Coef'
ge_c   = 'GE Lagged Capital Stock Coef'
wh_int = 'WH Intercept'
wh_f   = 'WH Lagged Share Value Coef'
wh_c   = 'WH Lagged Capital Stock Coef';
gei = ge_int + ge_f * gef + ge_c * gec;
whi = wh_int + wh_f * whf + wh_c * whc;
run;
```

The preceding PROC MODEL step defines the structural model and stores it in the model file named GRUNMOD.

The following PROC MODEL step reads in the model, adds the vector autoregressive terms using %AR, and requests SUR estimation by using the FIT statement.

```title2 'With Unrestricted Vector AR(1) Error Process';

proc model data=grunfeld model=grunmod;
%ar( ar, 1, gei whi )
fit gei whi / sur;
run;
```

The final PROC MODEL step estimates the restricted model, as shown in the following statements:

```title2 'With restricted AR(1) Error Process';

proc model data=grunfeld model=grunmod;
%ar( gei, 1 )
%ar( whi, 1)
fit gei whi / sur;
run;
```

Output 19.3.1: Model Summary for the Unrestricted Model

 Example of Vector AR(1) Error Process Using Grunfeld's Model With Unrestricted Vector AR(1) Error Process

The MODEL Procedure

Model Summary
Model Variables 6
Parameters 10
Equations 2
Number of Statements 7

Model Variables gei whi gef gec whf whc ge_int ge_f ge_c wh_int wh_f wh_c ar_l1_1_1(0) ar_l1_1_2(0) ar_l1_2_1(0) ar_l1_2_2(0) gei whi

The 2 Equations to Estimate
gei = F(ge_int, ge_f, ge_c, wh_int, wh_f, wh_c, ar_l1_1_1, ar_l1_1_2)
whi = F(ge_int, ge_f, ge_c, wh_int, wh_f, wh_c, ar_l1_2_1, ar_l1_2_2)

 NOTE: At SUR Iteration 9 CONVERGE=0.001 Criteria Met.

Output 19.3.2: Estimation Summary for the Unrestricted Model

 Example of Vector AR(1) Error Process Using Grunfeld's Model With Unrestricted Vector AR(1) Error Process

The MODEL Procedure
SUR Estimation Summary

Data Set Options
DATA= GRUNFELD

Minimization Summary
Parameters Estimated 10
Method Gauss
Iterations 9

Final Convergence Criteria
R 0.000609
PPC(wh_int) 0.002798
RPC(wh_int) 0.005411
Object 6.243E-7
Trace(S) 720.2454
Objective Value 1.374476

Observations
Processed
Solved 20

Output 19.3.3: Estimation Results for the Unrestricted Model

 Example of Vector AR(1) Error Process Using Grunfeld's Model With Unrestricted Vector AR(1) Error Process

The MODEL Procedure

Nonlinear SUR Summary of Residual Errors
Equation DF Model DF Error SSE MSE Root MSE R-Square Adj R-Sq Label
gei 5 15 9374.5 625.0 24.9993 0.7910 0.7352 Gross Investment GE
whi 5 15 1429.2 95.2807 9.7612 0.7940 0.7391 Gross Investment WH

Nonlinear SUR Parameter Estimates
Parameter Estimate Approx Std Err t Value Approx
Pr > |t|
Label
ge_int -42.2858 30.5284 -1.39 0.1863 GE Intercept
ge_f 0.049894 0.0153 3.27 0.0051 GE Lagged Share Value Coef
ge_c 0.123946 0.0458 2.70 0.0163 GE Lagged Capital Stock Coef
wh_int -4.68931 8.9678 -0.52 0.6087 WH Intercept
wh_f 0.068979 0.0182 3.80 0.0018 WH Lagged Share Value Coef
wh_c 0.019308 0.0754 0.26 0.8015 WH Lagged Capital Stock Coef
ar_l1_1_1 0.990902 0.3923 2.53 0.0233 AR(ar) gei: LAG1 parameter for gei
ar_l1_1_2 -1.56252 1.0882 -1.44 0.1716 AR(ar) gei: LAG1 parameter for whi
ar_l1_2_1 0.244161 0.1783 1.37 0.1910 AR(ar) whi: LAG1 parameter for gei
ar_l1_2_2 -0.23864 0.4957 -0.48 0.6372 AR(ar) whi: LAG1 parameter for whi

Output 19.3.4: Model Summary for the Restricted Model

 Example of Vector AR(1) Error Process Using Grunfeld's Model With restricted AR(1) Error Process

The MODEL Procedure

Model Summary
Model Variables 6
Parameters 8
Equations 2
Number of Statements 7

Model Variables gei whi gef gec whf whc ge_int ge_f ge_c wh_int wh_f wh_c gei_l1(0) whi_l1(0) gei whi

Output 19.3.5: Estimation Results for the Restricted Model

 Example of Vector AR(1) Error Process Using Grunfeld's Model With restricted AR(1) Error Process

The MODEL Procedure

Nonlinear SUR Summary of Residual Errors
Equation DF Model DF Error SSE MSE Root MSE R-Square Adj R-Sq Label
gei 4 16 10558.8 659.9 25.6890 0.7646 0.7204 Gross Investment GE
whi 4 16 1669.8 104.4 10.2157 0.7594 0.7142 Gross Investment WH

Nonlinear SUR Parameter Estimates
Parameter Estimate Approx Std Err t Value Approx
Pr > |t|
Label
ge_int -30.1239 29.7227 -1.01 0.3259 GE Intercept
ge_f 0.043527 0.0149 2.93 0.0099 GE Lagged Share Value Coef
ge_c 0.119206 0.0423 2.82 0.0124 GE Lagged Capital Stock Coef
wh_int 3.112671 9.2765 0.34 0.7416 WH Intercept
wh_f 0.053932 0.0154 3.50 0.0029 WH Lagged Share Value Coef
wh_c 0.038246 0.0805 0.48 0.6410 WH Lagged Capital Stock Coef
gei_l1 0.482397 0.2149 2.24 0.0393 AR(gei) gei lag1 parameter
whi_l1 0.455711 0.2424 1.88 0.0784 AR(whi) whi lag1 parameter