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In statistics, and particularly in econometrics, the reduced form of a system of equations is the result of solving the system for the endogenous variables. This gives the latter as a function of the exogenous variables, if any. In econometrics, "structural form" models begin from deductive theories of the economy, while "reduced form" models begin by identifying particular relationships between variables. Let ''Y'' and ''X'' be random vectors. ''Y'' is the vector of the variables to be explained (endogeneous variables) by a statistical model and ''X'' is the vector of explanatory (exogeneous) variables. In addition let be a vector of error terms. Then the general expression of a structural form is , where ''f'' is a function, possibly from vectors to vectors in the case of a multiple-equation model. The reduced form of this model is given by , with ''g'' a function. ==Structural form== As an example, we use a system of two equations. Both equations are linear. The system models the supply and demand of some specific good. The quantity of the demand varies inversely with the price: a higher price decreases demand. The quantity of the supply varies directly with the price: a higher price makes supply more profitable. In formulas: : supply: : demand: with positive ''bS'' and negative ''bD''. This The two endogenous variables are the traded quantity ''Q'' and the price ''P'', defined by the two equations of the system. Of course there are always as many endogenous variables as there are equations. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「reduced form」の詳細全文を読む スポンサード リンク
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