What are the two conditions for OVB?
In econometrics and statistical modeling, omitted variable bias occurs when a regression model leaves out a relevant independent variable that affects the dependent variable. For omitted variable bias to actively distort the estimated coefficients of the included variables, two strict conditions must be simultaneously satisfied. First, the omitted variable must be a true determinant of the dependent variable, meaning it directly impacts or influences the outcome variable being analyzed. Second, the omitted variable must be correlated with at least one of the independent variables already included in the regression model. When both of these conditions are met, the estimated effect of the included variable absorbs part of the impact from the missing factor, resulting in biased, inconsistent, and potentially misleading empirical conclusions.
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