Addressing Endogeneity in Ordered Logistic Models via Instrumental Variables: A Practical Guide in R

This study addresses the endogeneity issue in ordered response variables by developing a systematic estimation framework that combines two-stage residual inclusion (2SRI) with instrumental variable techniques. First, through simulation of datasets containing three endogenous independent variables, twelve potential instrumental variables, and multiple control variables, we validated the effectiveness of instrumental variable selection and model identification. Second, employing ordered logistic regression (OLR) for both stages of estimation successfully controlled endogeneity bias and enhanced the accuracy of causal inference. Empirical results demonstrate that the selected instrumental variables exhibit significant explanatory power over endogenous independent variables, with endogeneity issues alleviated after incorporating model residuals. This work not only provides a practical solution for handling endogeneity in ordered categorical dependent variables but also offers detailed R language implementation references for researchers in related fields, demonstrating both theoretical and practical value.