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In a statistical model, an endogenous parameter or endogenous variable is one that is correlated with the error term. Endogeneity can arise as a result of measurement error, autoregression with autocorrelated errors, simultaneity and omitted variables. Two common causes of endogeneity are: 1) an uncontrolled confounder causing both independent and dependent variables of a model; and 2) a loop of causality between the independent and dependent variables of a model. For example, in a simple supply and demand model, when predicting the quantity demanded in equilibrium, the price is endogenous because producers change their price in response to demand and consumers change their demand in response to price. In this case, the price variable is said to have total endogeneity once the demand and supply curves are known. In contrast, a change in consumer tastes or preferences would be an exogenous change on the demand curve. == Exogeneity versus endogeneity == In a stochastic model, the notion of the ''usual exogeneity'', ''sequential exogeneity'', ''strong/strict exogeneity'' can be defined. Exogeneity is articulated in such a way that a variable or variables is exogenous for parameter . Even if a variable is exogenous for parameter , it might be endogenous for parameter . When the explanatory variables are not stochastic, then they are strong exogenous for all the parameters. The problem of endogeneity occurs when the independent variable is correlated with the error term in a regression model. This implies that the regression coefficient in an Ordinary Least Squares (OLS) regression is biased, however if the correlation is not contemporaneous, then it may still be consistent. There are many methods of overcoming this, including instrumental variable regression and Heckman selection correction. 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「Endogeneity (econometrics)」の詳細全文を読む スポンサード リンク
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