Reduced form equations are equations derived from a system of structural equations by solving for the endogenous variables in terms of only the exogenous variables and error terms. They allow you to determine the value of endogenous variables given only external factors. Reduced form equations are useful for predicting outcomes when not all variables are directly observable or controllable.

1. 11.5. What is meant by a reduced form of (regression) equation? What is it use?
11.16. Consider the following two-equation model:
Y1t = A1 + A2Y2t + A3X1t + u1t
Y2t = B1 + B2Y1t + B3X2t + u2t
Where the Y
Solution
Q.11.5. In regession analysis, one tries to find out relationships between two or more variables
where some variables are endogenous and others are exogenous variables. Variables are
endogenpus when there a correlation exists between the variable and error term. Whereas,
exogenous variables are external variables.
When an regression model/equations is find out through statistical measure and any smaller set
of equation is formed from the model, it is known as reduced firms of equation. In other
words, the reduced form of a system of equations is the result of solving the system for the
endogenous variables.
The reduced forms of equations are used to find out the value of endogenous variables along
with other variables.