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Nonparametric Density and Regression Estimation Author info | Abstract | Publisher info | Download info | Related research | Statistics DiNardo, John
Tobias, Justin
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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.
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
12020.
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Date of creation: 27 Aug 2004Date of revision:
Publication status: Published in Journal of Economic Perspectives, 2001, Vol. 15, No. 4, pp. 11-28.Handle: RePEc:isu:genres:12020Contact details of provider: Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070 Phone: +1 515.294.6741 Fax: +1 515.294.0221 Email: Web page: http://www.econ.iastate.edu More information through EDIRC
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