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Fast Algorithms for Quantile Regression with Selection

Santiago Pereda‐Fernández

Journal of Econometric Methods2025https://doi.org/10.1515/jem-2024-0022article
AJG 1ABDC B
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0.50

What the paper says

The estimation of Quantile Regression with Selection (QRS) requires the estimation of the entire quantile process several times to estimate the parameters that model self-selection. Moreover, closed-form expressions of the asymptotic variance are too cumbersome, making the bootstrap more convenient to perform inference. I propose streamlined algorithms for the QRS estimator that significantly reduce computation time through preprocessing techniques and quantile grid reduction for the estimation of the parameters. I show the optimization enhancements and how they can improve the precision of the estimates without sacrificing computational efficiency with some simulations.

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https://doi.org/https://doi.org/10.1515/jem-2024-0022

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@article{santiago2025,
  title        = {{Fast Algorithms for Quantile Regression with Selection}},
  author       = {Santiago Pereda‐Fernández},
  journal      = {Journal of Econometric Methods},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1515/jem-2024-0022},
}

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Fast Algorithms for Quantile Regression with Selection

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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