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Nonparametric Density and Regression Estimation

  • DiNardo, John
  • Tobias, Justin

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: 01 Jan 2001
Date of revision:
Publication status: Published in Journal of Economic Perspectives 2001, vol. 15, pp. 11-28
Handle: RePEc:isu:genres:12020
Contact 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
Web page: http://www.econ.iastate.edu
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  1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  2. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
  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. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation for Research in Economics, Yale University.
  5. 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.
  6. Blackburn, McKinley L & Neumark, David, 1993. "Omitted-Ability Bias and the Increase in the Return to Schooling," Journal of Labor Economics, University of Chicago Press, vol. 11(3), pages 521-44, July.
  7. Yatchew, A., 1997. "An elementary estimator of the partial linear model," Economics Letters, Elsevier, vol. 57(2), pages 135-143, December.
  8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  9. 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.
  10. 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.
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