Quantiles for Counts

José António Machado and João Santos Silva

Econometrics from University Library of Munich, Germany

Abstract: This paper studies the estimation of conditional quantiles of counts. Given the discreteness of the data, some smoothness has to be artificially imposed on the problem. The methods currently available to estimate quantiles of count data either assume that the counts result from the discretization of a continuous process, or are based on a smoothed objective function. However, these methods have several drawbacks. We show that it is possible to smooth the data in a way that allows inference to be performed using standard quantile regression techniques. The performance and implementation of the estimator are illustrated by simulations and an application.

Keywords: Asymmetric maximum likelihood; Jittering; Maximum score estimator; Quantile regression; Smoothing. (search for similar items in EconPapers)
JEL-codes: C13 C25 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2003-03-05
New Economics Papers: this item is included in nep-cmp and nep-ecm
Note: Type of Document - Acrobat PDF; prepared on IBM PC ; pages: 39
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Citations: View citations in EconPapers (1)

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https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0303/0303001.pdf (application/pdf)

Related works:
Journal Article: Quantiles for Counts (2005)
Working Paper: Quantiles for counts (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0303001

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