Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data
By S. Ullah, Pervaiz Akhtar, Ghasem Zaefarian. 2018
1,014 citations recorded by the source corpus.
Abstract 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 coefficients 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 purposes, we used a dataset consisting of observations over a 15-year period (i.e., 2002 to 2016) from 101 UK listed companies and examined the direct effect of R&D expenditures, corporate governance, and firms' characteristics on performance. Due to endogeneity bias, the result of our analyses indicates significant differences in findings reported under the ordinary least square (OLS) approach, fixed effects and the generalized method of moments (GMM) estimations. We also provide generic STATA commands that can be utilized by marketing researchers in implementing a GMM model that better controls for the three sources of endogeneity, namely, unobserved heterogeneity, simultaneity and dynamic endogeneity.
Subfields: Accounting, Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/9481f4c0e529b1b5ddc630ec1971cb088b9cda7d
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Citations in the corpus
- Factors affecting corporate environmental disclosure in emerging markets: The role of corporate governance structures (2020, methodology): “Ullah et al. (2018) indicate that a crucial assumption for the validity of the two-step GMM method is that instruments (the lagged versions of dependent variables) are exogenous.”