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

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  • J. Myles Shaver

    (Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

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 .

Suggested Citation

  • J. Myles Shaver, 2019. "Interpreting Interactions in Linear Fixed-Effect Regression Models: When Fixed-Effect Estimates Are No Longer Within-Effects," Strategy Science, INFORMS, vol. 4(1), pages 25-40, March.
    • Handle: RePEc:inm:orstsc:v:4:y:2019:i:1:p:25-40
    DOI: 10.1287/stsc.2018.0065
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    References listed on IDEAS

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    1. J. Myles Shaver, 1998. "Accounting for Endogeneity When Assessing Strategy Performance: Does Entry Mode Choice Affect FDI Survival?," Management Science, INFORMS, vol. 44(4), pages 571-585, April.
    2. S. Trevis Certo & Michael C. Withers & Matthew Semadeni, 2017. "A tale of two effects: Using longitudinal data to compare within- and between-firm effects," Strategic Management Journal, Wiley Blackwell, vol. 38(7), pages 1536-1556, July.
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