Addressing Endogeneity Using a Two-Stage Copula Generated Regressor Approach
提出一种无需工具变量的两阶段Copula内生性校正方法，放宽了现有方法对非正态分布和独立性的要求，适用于更广泛的内生性场景。
The ubiquitous presence of endogenous regressors presents a significant challenge when drawing causal inferences using observational data. The classical econometric method used to handle regressor endogeneity requires instrumental variables (IVs) that must satisfy the stringent condition of exclusion restriction, rendering it unfeasible in many settings. Herein, the authors propose a new IV-free method that uses copulas to address the endogeneity problem. Existing copula correction methods require nonnormal endogenous regressors: Normally or nearly normally distributed endogenous regressors cause model nonidentification or significant finite-sample bias. Furthermore, existing copula control function methods presume the independence of exogenous regressors and endogenous regressors. The authors' generalized two-stage copula endogeneity-correction (2sCOPE) method simultaneously relaxes the two key identification requirements while maintaining the Gaussian copula regressor-error dependence structure. They prove that under the Gaussian copula dependence structure, 2sCOPE yields consistent causal-effect estimates with correlated endogenous and exogenous regressors as well as normally distributed endogenous regressors. In addition to relaxing the identification requirements, 2sCOPE has superior finite-sample performance and addresses the significant finite-sample bias problem due to insufficient regressor nonnormality. Moreover, 2sCOPE employs generated regressors derived from existing regressors to control for endogeneity, and can thus considerably increase the ease and broaden the applicability of IV-free methods for handling regressor endogeneity. The authors further demonstrate 2sCOPE's performance using simulation studies and illustrate its use in an empirical application.