In econometrics, endogeneity broadly refers to situations in which an explanatory variable is correlated with the error term. The distinction between endogenous and exogenous variables originated in simultaneous equations models, where one separates variables whose values are determined by the model from variables which are predetermined; ignoring simultaneity in the estimation leads to biased estimates as it violates the exogeneity assumption of the Gauss–Markov theorem. The problem of endogeneity is often ignored by researchers conducting non-experimental research and doing so precludes making policy recommendations. Instrumental variable techniques are commonly used to address this problem. Besides simultaneity, correlation between explanatory variables and the error term can arise when an unobserved or omitted variable is confounding both independent and dependent variables, or when independent variables are measured with error. 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. If the independent variable is correlated with the error term in a regression model then the estimate of the regression coefficient in an ordinary least squares (OLS) regression is biased; however if the correlation is not contemporaneous, then the coefficient estimate may still be consistent. There are many methods of correcting the bias, including instrumental variable regression and Heckman selection correction. Static models The following are some common sources of endogeneity. Omitted variable In this case, the endogeneity comes from an uncontrolled confounding variable, a variable that is correlated with both the independent variable in the model and with the error term. (Equivalently, the omitted variable affects the independent variable and separately affects the dependent variable.) Assume that the "true" model to be estimated is but is omitted from the regression model (perhaps because there is no way to measure it directly). Then the model that is actually estimated is where (thus, the term has been absorbed into the error term). If the correlation of and is not 0 and separately affects (meaning ), then is correlated with the error term . Here, is not exogenous for and , since, given , the distribution of depends not only on and , but also on