Interpreting Interactions in Linear Fixed-Effect Regression Models: When Fixed-Effect Estimates Are No Longer Within-Effects

J. Myles Shaver ()
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J. Myles Shaver: Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455

Strategy Science, 2019, vol. 4, issue 1, 25-40

Abstract: Fixed-effect regression models use within-firm variation to identify coefficient estimates, which is advantageous for mitigating certain endogeneity concerns and ruling out spurious relationships. I demonstrate that fixed-effect regression models with interaction terms (and by extension quadratic or higher-degree terms) confound within-firm and between-firm variation in identifying interaction coefficient estimates. Thus, in these specifications coefficient estimates lack a desirable property of standard fixed-effect estimates. I substantiate this concern using simulations and an empirical example. I also demonstrate how segmented regression aids assessing whether within-firm or between-firm variation identifies interaction coefficient estimates in fixed-effect models. The online appendix is available at https://doi.org/10.1287/stsc.2018.0065 .

Keywords: fixed-effect; research methods; interaction; quadratic; interpretation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orstsc:v:4:y:2019:i:1:p:25-40

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