Simultaneous causality
A)means you must run a second regression of X on Y.
B)leads to correlation between the regressor and the error term.
C)means that a third variable affects both Y and X.
D)cannot be established since regression analysis only detects correlation between variables.

Question

Quizplus · Accepted Answer

Simultaneous causality refers to a situation where both X and Y affect each other, which can lead to correlation between the regressor and the error term. This violates the assumption of the classical linear regression model and can lead to biased estimates of the regression coefficients. To address simultaneous causality, researchers may need to use instrumental variables or other advanced econometric techniques. A second regression of X on Y (A) would not necessarily help to address the issue of simultaneous causality. While a third variable (C) could be a potential factor in simultaneous causality, it is not a necessary condition. Finally, while regression analysis only detects correlation between variables (D), this does not mean that it cannot be used to infer causality under certain assumptions and conditions.

Simultaneous Causality