2013Strategic Management JournalRequires access

The perils of endogeneity and instrumental variables in strategy research: Understanding through simulations

Matthew Semadeni, Michael C. Withers, S. Trevis Certo

Open publisher page 745 citations

Abstract

In this paper we use simulations to examine how endogeneity biases the results reported by ordinary least squares ( OLS ) regression. In addition, we examine how instrumental variable techniques help to alleviate such bias. Our results demonstrate severe bias even at low levels of endogeneity. Our results also illustrate how instrumental variables produce unbiased coefficient estimates, but instrumental variables are associated with extremely low levels of statistical power. Finally, our simulations highlight how stronger instruments improve statistical power and that endogenous instruments can report results that are inferior to those reported by OLS regression. Based on our results, we provide a series of recommendations for scholars dealing with endogeneity . Copyright © 2013 John Wiley & Sons, Ltd.

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What this paper is about

In this paper we use simulations to examine how endogeneity biases the results reported by ordinary least squares ( OLS ) regression. In addition, we examine how instrumental variable techniques help to alleviate such bias. Our results demonstrate severe bias even at low levels of endogeneity. Our results also illustrate how instrumental variables produce unbiased coefficient estimates, but instrumental variables are associated with extremely low levels of statistical power. Finally, our simulations highlight how stronger instruments improve statistical power and that endogenous instruments can report results that are inferior to those reported by OLS regression. Based on our results, we provide a series of recommendations for scholars dealing with endogeneity . Copyright © 2013 John Wiley & Sons, Ltd.

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Available abstract

In this paper we use simulations to examine how endogeneity biases the results reported by ordinary least squares ( OLS ) regression. In addition, we examine how instrumental variable techniques help to alleviate such bias. Our results demonstrate severe bias even at low levels of endogeneity. Our results also illustrate how instrumental variables produce unbiased coefficient estimates, but instrumental variables are associated with extremely low levels of statistical power. Finally, our simulations highlight how stronger instruments improve statistical power and that endogenous instruments can report results that are inferior to those reported by OLS regression. Based on our results, we provide a series of recommendations for scholars dealing with endogeneity . Copyright © 2013 John Wiley & Sons, Ltd.

Key concepts: Endogeneity, Instrumental variable, Econometrics, Ordinary least squares, Economics, Omitted-variable bias, Variables, Regression

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