Density estimation using bootstrap quantile variance and quantile-mean covariance

Gabriel Montes-Rojas () and Andres Mena ()

No 2020-50, Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) from Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET)

Abstract: We propose two novel bootstrap density estimators based on the quantile variance and the quantile-mean covariance. We review previous developments on quantile-density estimation and asymptotic results in the literature that can be applied to this case. We conduct Monte Carlo simulations for dierent data generating processes, sample sizes, and parameters. The estimators perform well in comparison to benchmark nonparametric kernel density estimator. Some of the explored smoothing techniques present lower bias and mean integrated squared errors, which indicates that the proposed estimator is a promising strategy.

Keywords: Density Estimation; Quantile Variance; Quantile-Mean Covariance; Bootstrap (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C46 (search for similar items in EconPapers)
Date: 2020-02
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