COMPONENT Statement
COMPONENT name = (var1 var2 ...) * state ;
COMPONENT name = state[ integer ] ;
The COMPONENT statement specifies a component (a linear combination of state elements), named name. name can later be used as a right-hand side term in the MODEL statement, which defines the observation equation. Its estimate is output to the OUT= data set that is specified in the FORECAST statement.

The first form of the COMPONENT statement defines a component as a dot product of a state subsection state and a row vector (var1 var2 ...). The value of state must be the name of a state subsection defined by using a STATE statement elsewhere in the program. The row vector (var1 var2 ...), either a list of numbers or a list of variables, must be of the same dimension as the state subsection defined by state. This form—also called the explicit dot product form—of the component specification is unambiguous; however, it requires detailed knowledge of the state vector underlying the state specification. Suppose state0 is a two-dimensional state defined by a STATE statement elsewhere in the program and X1 and X2 are (numeric) predictor variables. The following are valid examples of the dot product form of the COMPONENT statement:

 component c1 = (x1 x2) * state0;
 component c2 = (1 1) * state0;

The second form of the COMPONENT statement is a shortened version of the first form; it defines the component as a particular element of state—for example, state[3] defines the component as the third element of state. The specified integer must lie between 1 and dim, the dimension of state. The second form of component specification has another important use when the STATE statement that defines state uses the TYPE= option to set its type. In this case the second form of the component specification assumes additional meaning for the types LL, SEASON, and CYCLE. For example, if state is a three-dimensional seasonal component, state[2] signifies an appropriate linear combination of state that results in the second of the three seasonals that constitute the three-dimensional seasonal. Similar interpretation holds for the types LL and CYCLE. See the sections Multivariate Season and Predefined Structural Models for more information.


Note: This procedure is experimental.