The Estimation of Conditional Densities

Xiaohong Chen (), Oliver Linton and Peter M Robinson

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading choices of bandwidths in numerator and denominator for the ability of the estimate to integrate to one and to have finite moments. Again bearing in mind different bandwidth possibilities, we discuss asymptotic theory for the estimate: asymptotic bias and variance are calculated under various conditions, an extended discussion of bandwidth choice is included, and a central limit theorem is given.

Keywords: Conditional density estimation; serial dependence; bandwidth choice. (search for similar items in EconPapers)
Date: 2001-05
References: Add references at CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://sticerd.lse.ac.uk/dps/em/em415.pdf (application/pdf)

Related works:
Working Paper: The estimation of conditional densities (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:415

Access Statistics for this paper

More papers in STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
Bibliographic data for series maintained by ().