The problem of endogeneity in econometric modeling: theoretical approaches and modern estimation methods

Abstract

This article systematizes the theoretical and methodological dimensions of endogeneity as a central problem in causal econometric analysis. It examines omitted variables, simultaneity and reverse causality, measurement error, unobserved heterogeneity, and dynamic panel structure as major sources of endogeneity. The study compares the Durbin–Wu–Hausman approach, instrumental variables, 2SLS, LIML, GMM, Difference GMM, System GMM, control-function methods, and modern IV procedures based on Double/Debiased Machine Learning. Since no independent empirical database is constructed, the article does not report artificial regression coefficients or fabricated significance levels; instead, its results are presented as a methodological synthesis of properties established in the econometric literature. The relative suitability, diagnostic requirements, and limitations of the estimators are evaluated for five typical endogeneity scenarios and summarized in comparative tables and a decision matrix. The central conclusion is that addressing endogeneity should begin with a credible identification strategy and valid, sufficiently strong instruments rather than with the mechanical selection of a more complex estimator.