Fast Algorithms for Quantile Regression with Selection

Pereda-Fernández Santiago ()
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Pereda-Fernández Santiago: Departamento de Economía, 16761 Universidad de Cantabria , Avenida de los Castros, s/n, 39005 Santander, Spain

Journal of Econometric Methods, 2025, vol. 14, issue 1, 35-47

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.

Keywords: copula; estimation algorithm; linear programming; Quantile Regression with Selection; rotated quantile regression (search for similar items in EconPapers)
JEL-codes: C31 C87 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/jem-2024-0022

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