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First-stage analysis for instrumental-variables quantile regression

Author

Listed:
  • Javier Alejo

    (IECON-Universidad de la República)

  • Antonio F. Galvao

    (Michigan State University)

  • Gabriel Montes-Rojas

    (CONICET and IIEP-BAIRES, Universidad de Buenos Aires)

Abstract

In this article, we develop a first-stage linear regression command, fsivqreg, for an instrumental-variables quantile regression (QR) model. The quan- tile first stage is analogous to the least-squares case, that is, a linear projection of the endogenous variables on the instruments and other exogenous covariates, with the difference that the QR case is a weighted projection. The weights are given by the conditional density function of the innovation term in the QR structural model, at a given quantile. An empirical application illustrates its implementation.

Suggested Citation

  • Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2024. "First-stage analysis for instrumental-variables quantile regression," Stata Journal, StataCorp LP, vol. 24(2), pages 273-286, June.
    • Handle: RePEc:tsj:stataj:v:24:y:2024:i:2:p:273-286
    DOI: 10.1177/1536867X241257803
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