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Nonparametric Density and Regression Estimation

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  • John DiNardo
  • Justin L. Tobias

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.

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File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.15.4.11
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Bibliographic Info

Article provided by American Economic Association in its journal Journal of Economic Perspectives.

Volume (Year): 15 (2001)
Issue (Month): 4 (Fall)
Pages: 11-28

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Handle: RePEc:aea:jecper:v:15:y:2001:i:4:p:11-28

Note: DOI: 10.1257/jep.15.4.11
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  1. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
  2. A. Yatchew, 2000. "Scale economies in electricity distribution: a semiparametric analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 187-210.
  3. Subramanian, Shankar & Deaton, Angus, 1996. "The Demand for Food and Calories," Journal of Political Economy, University of Chicago Press, vol. 104(1), pages 133-62, February.
  4. Tobias, Justin, 2001. "Are Returns to Schooling Concentrated Among the Most Able? A Semiparametric Analysis of the Ability-Earnings Relationships," Staff General Research Papers 12016, Iowa State University, Department of Economics.
  5. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
  6. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9206, Tilburg - Center for Economic Research.
  7. McKinley L. Blackburn & David Neumark, 1991. "Omitted-Ability Bias and the Increase in the Return to Schooling," NBER Working Papers 3693, National Bureau of Economic Research, Inc.
  8. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  9. Yatchew, A., 1997. "An elementary estimator of the partial linear model," Economics Letters, Elsevier, vol. 57(2), pages 135-143, December.
  10. Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228 Elsevier.
  11. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
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