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Root-n Consistent Kernel Density Estimation in Practice

Author

Listed:
  • Henderson Daniel J.

    (Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL 35487-0224, USA)

  • Parmeter Christopher F.

    (Department of Economics, University of Miami, USA)

Abstract

This paper details implementation of the recently proposed root-n kernel density estimator of (Escanciano, J. C., and D. T. Jacho-Chávez. 2012. “n$\sqrt n $-uniformly consistent density estimation in nonparametric regression models.” Journal of Econometrics 167: 305–316.) that circumvents the slow rate of convergence of traditional nonparametric kernel density estimators. We discuss implementation issues such as bandwidth selection and controlling for heteroskedasticity. Two empirical examples are provided; we re-examine the classic study of the emerging multimodality of the cross-country distribution of income per capita, finding more local structure with this new method, and we study the distribution of lean body mass across gender, where we demonstrate robustness of the new methods to alternative bandwidth selection mechanisms.

Suggested Citation

  • Henderson Daniel J. & Parmeter Christopher F., 2017. "Root-n Consistent Kernel Density Estimation in Practice," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-10, January.
    • Handle: RePEc:bpj:jecome:v:6:y:2017:i:1:p:10:n:2
    DOI: 10.1515/jem-2014-0010
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    References listed on IDEAS

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    1. Einmahl, John H.J. & Van Keilegom, Ingrid, 2008. "Specification tests in nonparametric regression," Journal of Econometrics, Elsevier, vol. 143(1), pages 88-102, March.
    2. Parmeter, Christopher F., 2008. "The effect of measurement error on the estimated shape of the world distribution of income," Economics Letters, Elsevier, vol. 100(3), pages 373-376, September.
    3. Bianchi, Marco, 1997. "Testing for Convergence: Evidence from Non-parametric Multimodality Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 393-409, July-Aug..
    4. Charles I. Jones, 1997. "On the Evolution of the World Income Distribution," Journal of Economic Perspectives, American Economic Association, vol. 11(3), pages 19-36, Summer.
    5. Quah, D., 1990. "Galton'S Fallacy And The Tests Of The Convergence Hypothesis," Working papers 552, Massachusetts Institute of Technology (MIT), Department of Economics.
    6. Daniel J. Henderson & R. Robert Russell, 2005. "Human Capital And Convergence: A Production-Frontier Approach ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1167-1205, November.
    7. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    8. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521279680, January.
    9. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    10. Daniel J. Henderson & Christopher F. Parmeter & R. Robert Russell, 2008. "Modes, weighted modes, and calibrated modes: evidence of clustering using modality tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 607-638.
    11. Rilstone, Paul, 1991. "Nonparametric Hypothesis Testing with Parametric Rates of Convergence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(1), pages 209-227, February.
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    Cited by:

    1. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    2. Henderson, Daniel J. & Sheehan, Alice, 2018. "Kernel-based testing with skewed and heavy-tailed data: Evidence from a nonparametric test for heteroskedasticity," Economics Letters, Elsevier, vol. 172(C), pages 8-11.

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    Keywords

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