Probing for omitted variable bias: The role of the impact threshold of a confounding variable in complementing instrumental variable estimations

Bendig, David; Hoke, Jonathan

Abstract

Endogeneity due to omitted variable bias presents a significant challenge in empirical marketing research. The instrumental variable (IV) estimation is a prevalent technique to identify this bias, but its correct application can be complex and demanding. This study presents the impact threshold of a confounding variable (ITCV) as a valuable tool for assessing the likelihood of omitted variable bias. Instead of replacing IV estimations, we propose that the ITCV should precede such advanced techniques, as the IV approach may be unnecessary if the ITCV suggests no significant concern for omitted variable bias. This study contributes to the field of empirical marketing research by (1) detailing the theoretical foundations and practical applications of the ITCV, making it accessible to all researchers, regardless of their statistical expertise; (2) comparing the ITCV directly with IV estimation techniques across key metrics; (3) providing an interdisciplinary guide with step-by-step instructions on how to implement the ITCV using Stata and R; (4) demonstrating the ITCV's effectiveness through empirical evidence using a hypothetical research model, thus underscoring its practical utility and promoting its wider adoption; and (5) offering comprehensive reporting guidelines for the ITCV, complete with graphical illustrations, tables, and references to relevant studies.