@article{10902/36978,
year = {2025},
month = {7},
url = {https://hdl.handle.net/10902/36978},
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.},
organization = {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).},
publisher = {Walter de Gruyter},
publisher = {Journal of Econometric Methods, 2025, 14(1),  35-47},
title = {Fast algorithms for Quantile Regression with Selection},
author = {Pereda Fernández, Santiago},
}