MODEL Procedure

Example 24.3 Vector AR(1) Estimation

(View the complete code for this example.)

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 24.3.1 through Output 24.3.5:


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

   ... 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 24.3.1: 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 Variables6
Parameters10
Equations2
Number of Statements7

Model Variablesgei whi gef gec whf whc
Parameters(Value)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)
Equationsgei 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 24.3.2: 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 Estimated10
MethodGauss
Iterations9

Final Convergence Criteria
R0.000609
PPC(wh_int)0.002798
RPC(wh_int)0.005411
Object6.243E-7
Trace(S)720.2454
Objective Value1.374476

Observations
Processed
Read20
Solved20


Output 24.3.3: 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 
EquationDF ModelDF ErrorSSEMSERoot MSER-SquareAdj R-SqLabel
gei5159374.5625.024.99930.79100.7352Gross Investment GE
whi5151429.295.28079.76120.79400.7391Gross Investment WH

Nonlinear SUR Parameter Estimates
ParameterEstimateApprox Std Errt ValueApprox
Pr > |t|
Label
ge_int-42.285830.5284-1.390.1863GE Intercept
ge_f0.0498940.01533.270.0051GE Lagged Share Value Coef
ge_c0.1239460.04582.700.0163GE Lagged Capital Stock Coef
wh_int-4.689318.9678-0.520.6087WH Intercept
wh_f0.0689790.01823.800.0018WH Lagged Share Value Coef
wh_c0.0193080.07540.260.8015WH Lagged Capital Stock Coef
ar_l1_1_10.9909020.39232.530.0233AR(ar) gei: LAG1 parameter for gei
ar_l1_1_2-1.562521.0882-1.440.1716AR(ar) gei: LAG1 parameter for whi
ar_l1_2_10.2441610.17831.370.1910AR(ar) whi: LAG1 parameter for gei
ar_l1_2_2-0.238640.4957-0.480.6372AR(ar) whi: LAG1 parameter for whi


Output 24.3.4: 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 Variables6
Parameters8
Equations2
Number of Statements7

Model Variablesgei whi gef gec whf whc
Parameters(Value)ge_int ge_f ge_c wh_int wh_f wh_c gei_l1(0) whi_l1(0)
Equationsgei whi


Output 24.3.5: 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 
EquationDF ModelDF ErrorSSEMSERoot MSER-SquareAdj R-SqLabel
gei41610558.8659.925.68900.76460.7204Gross Investment GE
whi4161669.8104.410.21570.75940.7142Gross Investment WH

Nonlinear SUR Parameter Estimates
ParameterEstimateApprox Std Errt ValueApprox
Pr > |t|
Label
ge_int-30.123929.7227-1.010.3259GE Intercept
ge_f0.0435270.01492.930.0099GE Lagged Share Value Coef
ge_c0.1192060.04232.820.0124GE Lagged Capital Stock Coef
wh_int3.1126719.27650.340.7416WH Intercept
wh_f0.0539320.01543.500.0029WH Lagged Share Value Coef
wh_c0.0382460.08050.480.6410WH Lagged Capital Stock Coef
gei_l10.4823970.21492.240.0393AR(gei) gei lag1 parameter
whi_l10.4557110.24241.880.0784AR(whi) whi lag1 parameter


Last updated: June 19, 2025