Skip to main content

Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data

Ullah, Subhan, Akhtar, Pervaiz, Zaefarian, Ghasem (2018) Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71 . pp. 69-78. ISSN 0019-8501. (doi:10.1016/j.indmarman.2017.11.010) (KAR id:75438)

Language: English

Download (319kB) Preview
[thumbnail of Dealing with endogeneity bias-GMM method.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL


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.

Item Type: Article
DOI/Identification number: 10.1016/j.indmarman.2017.11.010
Uncontrolled keywords: Endogeneity bias, Generalized method of moments, Methodological issues, Panel data
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Pervaiz Akhtar
Date Deposited: 16 Aug 2019 14:32 UTC
Last Modified: 16 Feb 2021 14:06 UTC
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):


Downloads per month over past year