Fast Algorithms for Quantile Regression with Selection
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Santiago Pereda-Fernández
Abstract
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.
Funding source: Ministerio de Universidades
Award Identifier / Grant number: RMZ-18
Funding source: Ministerio de Ciencia e Innovación
Award Identifier / Grant number: MCIN/AEI/10.13039/501100011033
Acknowledgments
I would like to thank Manuel Arellano Stéphane Bonhomme, and Domenico Depalo for helpful comments and discussion. This work is part of the I + D + i project Ref. TED2021-131763A-I00 financed by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. I gratefully acknowledge financial support from the Spanish Ministry of Universities and the European Union-NextGenerationEU (RMZ-18).
References
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- The Story of a Model: The First-Order Diagonal Bilinear Autoregression
- Maximum Likelihood Estimation of Regression Effects in State Space Models
- Software
- QR.break: An R Package for Structural Breaks in Quantile Regression
- Practitioner's Corner
- Fast Algorithms for Quantile Regression with Selection
Articles in the same Issue
- Frontmatter
- Research Articles
- The Story of a Model: The First-Order Diagonal Bilinear Autoregression
- Maximum Likelihood Estimation of Regression Effects in State Space Models
- Software
- QR.break: An R Package for Structural Breaks in Quantile Regression
- Practitioner's Corner
- Fast Algorithms for Quantile Regression with Selection
