Smoothing quantile regressions

Resumo

We propose to smooth the entire objective function rather than only the check function in a linear quantile regression context. We derive a uniform Bahadur-Kiefer representation for the resulting convolution-type kernel estimator that demonstrates it improves on the extant quantile regression estimators in the literature. In addition, we also show that it is straightforward to compute asymptotic standard errors for the quantile regression coefficient estimates as well as to implement Wald-type tests. Simulations confirm that our smoothed quantile regression estimator performs very well in finite samples.

Ficha do documento

Tipo

Estudo

Ano

2017

Instituição

Escola de Economia de São Paulo

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso aberto

Identificador

oai:repositorio.fgv.br:10438/18390

Temas

EconomiaDados

Palavras-chave

Asymptotic expansionBahadur-Kiefer representationConditional quantileConvolution-based smoothingData-driven bandwidthEconomiaAnálise de regressãoSuavização (Estatística)