Reverse causality bias

In econometrics, an endogeneity problem occurs when an explanatory variable is correlated with the error term. Endogeneity can arise as a result of measurement error, autoregression with errors, simultaneous causality (see Instrumental variable), omitted selection, 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. The problem of endogeneity is very serious and oftentimes ignored by researchers conducting non-experimental research.

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.

In a , 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 ${\displaystyle \alpha }$. Even if a variable is exogenous for parameter ${\displaystyle \alpha }$, it might be endogenous for parameter ${\displaystyle \beta }$.

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