Addressing Endogeneity Using a Two-Stage Copula Generated Regressor Approach.

Area of Science:

• Econometrics
• Causal Inference
• Statistical Modeling

Background:

• Endogenous regressors pose challenges for causal inference in observational data.
• Traditional instrumental variable (IV) methods have strict requirements, limiting their applicability.
• Existing copula methods fail with normally distributed regressors or correlated exogenous/endogenous variables.

Purpose of the Study:

• To propose a novel instrumental variable-free method for addressing regressor endogeneity.
• To relax identification requirements of existing copula correction techniques.
• To improve the finite-sample performance and applicability of endogeneity correction methods.

Main Methods:

• Development of the generalized two-stage copula endogeneity-correction (2sCOPE) method.
• Relaxation of non-normality and independence assumptions for endogenous regressors.
• Utilizing generated regressors for endogeneity control within a Gaussian copula framework.

Main Results:

• 2sCOPE provides consistent causal-effect estimates with normally distributed endogenous regressors.
• The method achieves consistency even when endogenous and exogenous regressors are correlated.
• Demonstrated superior finite-sample performance and mitigation of bias compared to existing methods.

Conclusions:

• 2sCOPE effectively addresses regressor endogeneity without instrumental variables.
• The method broadens the applicability of IV-free techniques for causal inference.
• Simulation studies and empirical application validate the performance of 2sCOPE.