Nonparametric Density and Regression Estimation

John Enrico DiNardo and Justin Tobias ()

Journal of Economic Perspectives, 2001, vol. 15, issue 4, pages 11-28

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.

JEL-codes: C14 C22 C20 J24 (search for similar items in EconPapers)
Date: 2001
Note: DOI: 10.1257/jep.15.4.11
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