Endogennoci

Endogennoci, commonly referred to as endogeneity in econometrics, is a property of a statistical model in which one or more explanatory variables are correlated with the error term of the model. This correlation violates the standard exogeneity assumption that underpins ordinary least squares estimation and can lead to biased and inconsistent coefficient estimates.

Endogeneity can arise from several sources. Omitted variable bias occurs when a relevant, unobserved factor affects both the predictor and the outcome. Measurement error in the explanatory variable can create a correlation with the error term. Simultaneity or reverse causation happens when the dependent variable also influences the explanatory variable. Sample selection bias and incorrectly specified functional form can also generate endogeneity.

The presence of endogeneity compromises causal interpretation: estimated effects may reflect unobserved confounders rather than a true causal influence. It also distorts standard errors, making hypothesis tests unreliable and confidence intervals invalid.

Diagnostics and tests for endogeneity include the Durbin-Wu-Hausman test, which compares estimators from models with and without potential endogeneity, and tests related to instrumental variables to assess exogeneity and relevance. In models with instrumental variables, first-stage statistics help gauge instrument strength, and overidentification tests assess instrument validity.

Common remedies focus on obtaining consistent estimates. Instrumental variables methods, especially two-stage least squares (2SLS) and generalized method of moments (GMM), are widely used. In panel data, fixed effects and difference-in-differences designs can mitigate endogeneity from time-invariant unobserved factors. Model refinement, better measurement, and control function approaches are additional strategies.

Example: estimating the effect of education on earnings can be biased if ability is unobserved; valid instruments like proximity to colleges can help identify causal effects under appropriate assumptions.