This setting lets you change the way you view articles. You can choose to have articles open in a dialog window, a new tab, or directly in the same window.
Open in Dialog
Open in New Tab
Open in same window
Open in New Tab
Open in same window
Journal of Economic Perspectives: Vol. 15 No. 4 (Fall 2001)
JEP Volume. 15, Issue 4 | 
Previous ArticleNext Article
Sign up for Email Alerts Follow us on Twitter
Full-text Article (Complimentary)
View Comments on This Article (0) | Login to post a comment
Previous ArticleNext Article
Expand
Quick Tools:
Print Article Summary Email Link to this Article Export CitationSign up for Email Alerts Follow us on Twitter
Explore:
Nonparametric Density and Regression Estimation
Article Citation
DiNardo, John, and
Justin L. Tobias. 2001. "Nonparametric Density and Regression Estimation."
Journal of Economic Perspectives,
15(4): 11-28.
DOI: 10.1257/jep.15.4.11
DOI: 10.1257/jep.15.4.11
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.
Article Full-Text Access
Full-text Article (Complimentary)
Authors
DiNardo, John (U MI)
Tobias, Justin L. (U CA, Irvine)
Tobias, Justin L. (U CA, Irvine)
JEL Classifications
C14: Semiparametric and Nonparametric Methods
C20: Single Equation Models; Single Variables: General
C20: Single Equation Models; Single Variables: General
Comments
View Comments on This Article (0) | Login to post a comment
