In the econometrics literature, the VARMAX(,,) model is sometimes written in a form that is slightly different than the one shown in the previous section. This alternative form is referred to as a dynamic simultaneous equations model or a dynamic structural equations model.
Since is assumed to be positivedefinite, there exists a lower triangular matrix with ones on the diagonals such that , where is a diagonal matrix with positive diagonal elements.

where , , , and .
As an alternative form,

where , , , and has a diagonal covariance matrix . The PRINT=(DYNAMIC) option returns the parameter estimates that result from estimating the model in this form.
A dynamic simultaneous equations model involves a leading (lower triangular) coefficient matrix for at lag 0 or a leading coefficient matrix for at lag 0. Such a representation of the VARMAX(,,) model can be more useful in certain circumstances than the standard representation. From the linear combination of the dependent variables obtained by , you can easily see the relationship between the dependent variables in the current time.
The following statements provide the dynamic simultaneous equations of the VAR(1) model.
proc iml; sig = {1.0 0.5, 0.5 1.25}; phi = {1.2 0.5, 0.6 0.3}; /* simulate the vector time series */ call varmasim(y,phi) sigma = sig n = 100 seed = 34657; cn = {'y1' 'y2'}; create simul1 from y[colname=cn]; append from y; quit; data simul1; set simul1; date = intnx( 'year', '01jan1900'd, _n_1 ); format date year4.; run;
proc varmax data=simul1; model y1 y2 / p=1 noint print=(dynamic); run;
This is the same data set and model used in the section Getting Started: VARMAX Procedure. You can compare the results of the VARMA model form and the dynamic simultaneous equations model form.
Figure 36.25: Dynamic Simultaneous Equations (DYNAMIC Option)
Covariances of Innovations  

Variable  y1  y2 
y1  1.28875  0.00000 
y2  0.00000  1.29578 
AR  

Lag  Variable  y1  y2 
0  y1  1.00000  0.00000 
y2  0.30845  1.00000  
1  y1  1.15977  0.51058 
y2  0.18861  0.54247 
Dynamic Model Parameter Estimates  

Equation  Parameter  Estimate  Standard Error 
t Value  Pr > t  Variable 
y1  AR1_1_1  1.15977  0.05508  21.06  0.0001  y1(t1) 
AR1_1_2  0.51058  0.07140  7.15  0.0001  y2(t1)  
y2  AR0_2_1  0.30845  y1(t)  
AR1_2_1  0.18861  0.05779  3.26  0.0015  y1(t1)  
AR1_2_2  0.54247  0.07491  7.24  0.0001  y2(t1) 
In Figure 36.4 in the section Getting Started: VARMAX Procedure, the covariance of estimated from the VARMAX model form is

Figure 36.25 shows the results from estimating the model as a dynamic simultaneous equations model. By the decomposition of , you get a diagonal matrix () and a lower triangular matrix () such as where

The lower triangular matrix () is shown in the left side of the simultaneous equations model. The parameter estimates in equations system are shown in the right side of the twoequations system.
The simultaneous equations model is written as

The resulting twoequation system can be written as





