What Is Endogeneity
Table Of Contents:
- What Is Endogeneity?
- Why Does Endogeneity occur?
- How To Avoid Endogeneity?
(1) What Is Endogeneity?
- Endogeneity refers to a situation in which there is a correlation or relationship between an explanatory variable and the error term in a statistical model.
- This correlation can arise when the explanatory variable is influenced by factors that are not accounted for in the model, leading to biased and inconsistent estimates of the model parameters.
(2) Why Does Endogeneity Occur ?
In the context of regression analysis, endogeneity can occur when one or more of the following conditions are present:
Simultaneity: Simultaneity occurs when there is a mutual relationship between two or more variables, and the relationship operates in both directions. In this case, the dependent variable may affect the independent variable(s), and vice versa. This creates a feedback loop that violates the assumption of exogeneity, where the independent variable(s) should be unrelated to the error term.
Omitted Variable Bias: Omitted variable bias arises when relevant variables are not included in the regression model. If these omitted variables are correlated with both the dependent variable and the included independent variables, they can introduce endogeneity. The omitted variables can confound the relationship between the included variables and the dependent variable, leading to biased and inconsistent parameter estimates.
Measurement Error: Measurement error in the independent variable(s) can introduce endogeneity. If the measurement error is correlated with the error term, it can bias the estimates of the coefficients in the regression model. This occurs because the measured values of the independent variable(s) do not accurately represent their true values.
Reverse Causality: Reverse causality occurs when the direction of causality between the dependent variable and the independent variable(s) is opposite to what is assumed in the model. In other words, the dependent variable affects the independent variable(s), rather than the independent variable(s) affecting the dependent variable. This violates the assumption of exogeneity and leads to endogeneity.
(3) How To Avoid Endogeneity?
- Endogeneity can have serious consequences for regression analysis. It results in biased and inconsistent parameter estimates, making it challenging to draw valid causal inferences or make accurate predictions.
- To address endogeneity, various econometric techniques can be employed, such as instrumental variable (IV) regression, difference-in-differences (DD) analysis, or panel data methods.
- These techniques aim to mitigate the endogeneity problem by using instrumental variables, control groups, or fixed effects to isolate the causal relationship of interest.