Nonparametric Density and Regression Estimation
2001; American Economic Association; Volume: 15; Issue: 4 Linguagem: Inglês
10.1257/jep.15.4.11
ISSN1944-7965
AutoresJohn DiNardo, Justin L. Tobias,
Tópico(s)Advanced Statistical Methods and Models
ResumoWe 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.
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