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dc.contributor.authorChernozhukov, Victoren_US
dc.contributor.authorFernandez-Val, Ivanen_US
dc.contributor.authorMelly, Blaiseen_US
dc.date.accessioned2020-04-17T18:33:55Z
dc.date.available2020-04-17T18:33:55Z
dc.identifier.citationVictor Chernozhukov, Ivan Fernandez-Val, Blaise Melly. "Fast Algorithms for the Quantile Regression Process." ArXiv preprint, Volume arXiv:1901.03821,
dc.identifier.urihttps://hdl.handle.net/2144/40243
dc.description.abstractThe widespread use of quantile regression methods depends crucially on the existence of fast algorithms. Despite numerous algorithmic improvements, the computation time is still non-negligible because researchers often estimate many quantile regressions and use the bootstrap for inference. We suggest two new fast algorithms for the estimation of a sequence of quantile regressions at many quantile indexes. The first algorithm applies the preprocessing idea of Portnoy and Koenker (1997) but exploits a previously estimated quantile regression to guess the sign of the residuals. This step allows for a reduction of the effective sample size. The second algorithm starts from a previously estimated quantile regression at a similar quantile index and updates it using a single Newton-Raphson iteration. The first algorithm is exact, while the second is only asymptotically equivalent to the traditional quantile regression estimator. We also apply the preprocessing idea to the bootstrap by using the sample estimates to guess the sign of the residuals in the bootstrap sample. Simulations show that our new algorithms provide very large improvements in computation time without significant (if any) cost in the quality of the estimates. For instance, we divide by 100 the time required to estimate 99 quantile regressions with 20 regressors and 50,000 observations.en_US
dc.description.urihttps://arxiv.org/abs/1901.03821
dc.language.isoen_US
dc.relation.ispartofArXiv preprint
dc.subjectQuantile regressionen_US
dc.subjectQuantile regression processen_US
dc.subjectPreprocessingen_US
dc.subjectOne-step estimatoren_US
dc.subjectBootstrapen_US
dc.subjectUniform inferenceen_US
dc.titleFast algorithms for the quantile regression processen_US
dc.typeArticleen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusSubmitteden_US
dc.description.oaversionFirst author draft
dc.identifier.mycv511927


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