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Prediction in linear index models with endogenous regressors

Author

Listed:
  • Christopher L. Skeels

    (University of Melbourne)

  • Larry W. Taylor

    (Lehigh University)

Abstract

In this article, we examine prediction in the context of linear index models when one or more of the regressors are endogenous. To facilitate both within-sample and out-of-sample predictions, Stata offers the postestimation command predict (see [R] predict). We believe that the usefulness of the predictions provided by this command is limited, especially if one is interested in out-of-sample predictions. We demonstrate our point using a probit model with continuous endogenous regressors, although it clearly generalizes readily to other linear index models. We subsequently provide a program that offers one possible implementation of a new command, ivpredict, that can be used to address this shortcoming of predict, and we then illustrate its use with an empirical example. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Christopher L. Skeels & Larry W. Taylor, 2015. "Prediction in linear index models with endogenous regressors," Stata Journal, StataCorp LP, vol. 15(3), pages 627-644, September.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:3:p:627-644
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    Citations

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    Cited by:

    1. Abayomi Samuel Oyekale & Thonaeng Charity Maselwa, 2021. "An Instrumental Variable Probit Modeling of COVID-19 Vaccination Compliance in Malawi," IJERPH, MDPI, vol. 18(24), pages 1-14, December.
    2. Kerstin Bruckmeier & Regina T. Riphahn & Jürgen Wiemers, 2021. "Misreporting of program take-up in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," Empirical Economics, Springer, vol. 61(3), pages 1567-1616, September.
    3. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).

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