Nonparametric Density and Regression Estimation

John DiNardo and Justin Tobias ()

Staff General Research Papers Archive from Iowa State University, Department of Economics

Abstract: We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to "simpler" parametric methods.

Date: 2001-01-01
References: Add references at CitEc
Citations: View citations in EconPapers (136)

Published in Journal of Economic Perspectives 2001, vol. 15, pp. 11-28

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Nonparametric Density and Regression Estimation (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:isu:genres:12020

Access Statistics for this paper

More papers in Staff General Research Papers Archive from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().