OUTMODEL= Data Set

The STATESPACE Procedure

OUTMODEL= Data Set

The OUTMODEL= data set contains the estimates of the F and G matrices and their standard errors, the names of the components of the state vector, and the estimates of the innovation covariance matrix. The variables contained in the OUTMODEL= data set are as follows:

the BY variables
STATEVEC, a character variable containing the name of the component of the state vector corresponding to the observation. The STATEVEC variable has the value STD for standard deviations observations, which contain the standard errors for the estimates given in the preceding observation.
F_j, numeric variables containing the columns of the F matrix. The variable F_j contains the jth column of F. The number of F_j variables is equal to the value of the DIMMAX= option. If the model is of smaller dimension, the extraneous variables are set to missing.
G_j, numeric variables containing the columns of the G matrix. The variable G_j contains the jth column of G. The number of G_j variables is equal to r, the dimension of x_t given by the number of variables in the VAR statement.
SIG_j, numeric variables containing the columns of the innovation covariance matrix. The variable SIG_j contains the jth column of ${{{\Sigma}_{ee}}}$ .There are r variables SIG_j.

Table 18.2 shows an example of the OUTMODEL= data set, with x_t = (x_t,y_t)', ${z_{t}=(x_{t},y_{t},x_{t+1| t})'}$ ,and DIMMAX=4. In Table 18.2, F_i,j and G_i,j are the i,jth elements of F and G respectively. Note that all elements for F_4 are missing because F is a 3 ×3 matrix.

Table 18.2: Value in the OUTMODEL= Data Set

Obs	STATEVEC	F_1	F_2	F_3	F_4	G_1	G_2	SIG_1	SIG_2
1	X(T;T)	0	0	1	.	1	0	${\Sigma}$ _1,1	${\Sigma}$ _1,2
2	STD	.	.	.	.	.	.	.	.
3	Y(T;T)	F_2,1	F_2,2	F_2,3	.	0	1	${\Sigma}$ _2,1	${\Sigma}$ _2,2
4	STD	std F_2,1	std F_2,2	std F_2,3	.	.	.	.	.
5	X(T+1;T)	F_3,1	F_3,2	F_3,3	.	G_3,1	G_3,2	.	.
6	STD	std F_3,1	std F_3,2	std F_3,3	.	std G_3,1	std G_3,2	.	.

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