arXiv 26 Feb 2024 · Econometrics · publishedJournal of Econometric Methods (2025)
arXiv:2402.16693 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses computational challenges in estimating Quantile Regression with Selection (QRS). The estimation of the parameters that model self-selection requires the estimation of the entire quantile process several times. Moreover, closed-form expressions of the asymptotic variance are too cumbersome, making the bootstrap more convenient to perform inference. Taking advantage of recent advancements in the estimation of quantile regression, along with some specific characteristics of the QRS estimation problem, I propose streamlined algorithms for the QRS estimator. These algorithms significantly reduce computation time through preprocessing techniques and quantile grid reduction for the estimation of the copula and slope parameters. I show the optimization enhancements with some simulations. Lastly, I show how preprocessing methods can improve the precision of the estimates without sacrificing computational efficiency. Hence, they constitute a practical solutions for estimators with non-differentiable and non-convex criterion functions such as those based on copulas.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., I. Fernández-Val, and B. Melly (2022) Fast algorithms for the quantile regression process | 1.000 | 10 | 3 | 100% |
| 2 | Portnoy, S. and R. Koenker (1997) The gaussian hare and the laplacian tortoise: computability of squared-error versus absolute-error estimators | 1.000 | 8 | 4 | 100% |
| 3 | Arellano, M. and S. Bonhomme (2017) Quantile selection models with an application to understanding changes in wage inequality | 1.000 | 7 | 3 | 100% |
| 4 | Pereda-Fernández, S (2023) Identification and estimation of triangular models with a binary treatment | 0.737 | 3 | 2 | 100% |
| 5 | Koenker, R. and G. Bassett (1978) Regression quantiles | 0.644 | 2 | 2 | 100% |
| 6 | Pereda-Fernández, S (2022) Decomposition of differences in distribution under sample selection and the gender wage gap | 0.644 | 2 | 2 | 100% |
| 7 | Arellano, M. and S. Bonhomme (2017) Sample selection in quantile regression: a survey | 0.405 | 1 | 1 | 100% |
| 8 | Chen, S. and Q. Wang (2022) Quantile regression with censoring and sample selection | 0.405 | 1 | 1 | 100% |
| 9 | Ma, S. and M. R. Kosorok (2005) Robust semiparametric m-estimation and the weighted bootstrap | 0.405 | 1 | 1 | 100% |
| 10 | Sancetta, A. and S. Satchell (2004) The bernstein copula and its applications to modeling and approximations of multivariate distributions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 10 scored citations.