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Endogeneity and endogenous independent variables

Endogeneity refers to a scenario where the error term, representing omitted causes in a regression model, correlates with independent variables included in the model. Endogeneity, often overlooked in the past, is now receiving increased attention, with many journals requiring explicit consideration of this issue in submissions. The challenge lies in its identification, as it cannot be directly tested from regression results, necessitating more advanced modeling techniques. Endogeneity is introduced through an experimental design perspective, highlighting the importance of exogenous assignment (R) in distinguishing causal effects. If R, the random assignment to treatment or control groups, is influenced by the study variable (e.g., individuals' health in a medical study), it becomes endogenous, compromising causal interpretation. In multiple regression contexts, endogeneity is further explained through the error term in a regression model. If the error term, representing unmodeled causes of the dependent variable, correlates with any explanatory variables, it leads to endogeneity, making OLS regression biased and inconsistent. The video outlines three mechanisms leading to endogeneity: a common cause not included in the model, a correlation between an explanatory variable and unmodeled causes of the dependent variable, and simultaneity or reciprocal causation between variables. The concept is exemplified with cases like CEO gender and profitability and the impact of strategic deviation on firm profitability. These examples demonstrate how omitting relevant variables (e.g., market share) leads to omitted variable bias, making an explanatory variable like strategic deviation endogenous. The video concludes by emphasizing the importance of addressing endogeneity, either empirically using instrumental variables or theoretically, to ensure unbiased and consistent estimates in regression analysis. Slides: https://osf.io/df8z7