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Quantile Regression under Limited Dependent Variable

Author

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  • Javier Alejo
  • Gabriel Montes-Rojas

Abstract

A new Stata command, ldvqreg, is developed to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimators are implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it correctly estimates the parameters and it should be implemented instead of the available quantile regression methods when censoring is present. An empirical application to women's labor supply in Uruguay is considered.

Suggested Citation

  • Javier Alejo & Gabriel Montes-Rojas, 2021. "Quantile Regression under Limited Dependent Variable," Papers 2112.06822, arXiv.org.
    • Handle: RePEc:arx:papers:2112.06822
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    File URL: http://arxiv.org/pdf/2112.06822
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    References listed on IDEAS

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