Quantiles for Counts
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.
|Date of creation:||05 Mar 2003|
|Date of revision:|
|Note:||Type of Document - Acrobat PDF; prepared on IBM PC ; pages: 39|
|Contact details of provider:|| Web page: http://econwpa.repec.org|
When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpem:0303001. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)
If references are entirely missing, you can add them using this form.