A Practical Guide to Endogeneity Correction Using Copulas.

Area of Science:

• Econometrics
• Statistical Modeling
• Causal Inference

Background:

• Endogeneity, stemming from endogenous regressors, poses a significant challenge in causal inference.
• Traditional methods rely on instrumental variables with strict exclusion restrictions.
• Instrument-free copula methods have emerged as a powerful alternative for handling endogeneity.

Purpose of the Study:

• To provide a practical guide for applying copula methods to correct for endogeneity.
• To outline the theoretical underpinnings, benefits, and drawbacks of copula endogeneity correction.
• To discuss recent advancements and implementation details for robust application.

Main Methods:

• Overview of copula endogeneity correction techniques.
• Discussion of control function and likelihood-based joint estimation.
• Guidance on handling complex regressors (higher-order, non-continuous) and data structures (panel, nonlinear models).

Main Results:

• Copula correction offers a flexible, instrument-free approach to endogeneity.
• Recent advances improve the understanding, applicability, and robustness of copula methods.
• Implementation guidance covers essential aspects for practical use.

Conclusions:

• Copula correction methods provide a valuable tool for researchers facing endogeneity.
• Understanding data requirements and identification assumptions is crucial for effective application.
• This guide facilitates the appropriate use of copula correction for reliable causal inference.