Endogeneity bias can lead to inconsistent estimates and incorrect inferences, which may provide misleading conclusions and inappropriate theoretical interpretations. Sometimes such bias can even lead to coefcients having the wrong sign. Although this is a long-standing issue, it is now emerging in marketing and management science, with high-ranked journals increasingly exploring the issue. In this paper we methodologically demonstrate how to detect and deal with endogeneity issues in panel data.

For illustration purpose, we used a dataset consisting of 15 years of observations (i.e., 2002 to 2016) from 101 UK listed companies, and examined the direct effect of R&D expenditures, corporate governance, and rms’ characteristics on rm performance. The result of our analyses indicate signicant differences in our ndings reported under ordinary least square (OLS), xed effects and the generalized method of moments (GMM) estimations, due to endogeneity bias. We provide generic STATA commands that can be used by marketing researchers in implementing a GMM model that better controls for the three sources of endogeneity, namely, unobserved heterogeneity, simultaneity and dynamic endogeneity.