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Heterogeneous endogeneity

Author

Listed:
  • Pallab Ghosh

    (University of Oklahoma)

  • Kevin Grier

    (Texas Tech University)

  • Jaeho Kim

    (University of Oklahoma)

Abstract

We define heterogeneous endogeneity as the case where a potentially endogenous regressor is endogenous for some sub-groups of the data but exogenous for other subgroups. We derive an estimator and test procedure based on the control function approach to deal with the phenomenon. We show that accounting for heterogeneous endogeneity can greatly increase the power of endogeneity tests and increase the precision of our estimator over traditional IV. While the gains get larger as the instrument gets weaker and as the relative size of the non-endogenous subgroup gets larger, we find efficiency gains even when the underlying instrument is very strong. We illustrate our approach with an example using data from Abaide et al. (Econometrica 70:91–117).

Suggested Citation

  • Pallab Ghosh & Kevin Grier & Jaeho Kim, 2021. "Heterogeneous endogeneity," Statistical Papers, Springer, vol. 62(2), pages 847-886, April.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:2:d:10.1007_s00362-019-01116-9
    DOI: 10.1007/s00362-019-01116-9
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Qasim, 2024. "A weighted average limited information maximum likelihood estimator," Statistical Papers, Springer, vol. 65(5), pages 2641-2666, July.

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