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Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data

Author

Listed:
  • Chi-Yang Chu

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

  • Daniel J. Henderson

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

  • Christopher F. Parmeter

    (Department of Economics, University of Miami, Coral Gables, FL 33124-6520, USA)

Abstract

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that plug-in bandwidths are relatively small. Several empirical examples show that the plug-in bandwidths are typically similar in magnitude to their cross-validated counterparts.

Suggested Citation

  • Chi-Yang Chu & Daniel J. Henderson & Christopher F. Parmeter, 2015. "Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data," Econometrics, MDPI, vol. 3(2), pages 1-16, March.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:2:p:199-214:d:47581
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    References listed on IDEAS

    as
    1. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    2. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253, January.
    3. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    4. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    5. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
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    Cited by:

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    2. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    3. Byeong U. Park & Léopold Simar & Valentin Zelenyuk, 2020. "Forecasting of recessions via dynamic probit for time series: replication and extension of Kauppi and Saikkonen (2008)," Empirical Economics, Springer, vol. 58(1), pages 379-392, January.
    4. Regmi, Krishna & J. Henderson, Daniel, 2019. "Labor demand shocks at birth and cognitive achievement during childhood," Economics of Education Review, Elsevier, vol. 73(C).
    5. Henderson, Daniel J. & Sperlich, Stefan, 2022. "A Complete Framework for Model-Free Difference-in-Differences Estimation," IZA Discussion Papers 15799, Institute of Labor Economics (IZA).
    6. Lynda Harfouche & Smail Adjabi & Nabil Zougab & Benedikt Funke, 2018. "Multiplicative bias correction for discrete kernels," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 253-276, June.
    7. Byeong U. Park & Leopold Simar & Valentin Zelenyuk, 2017. "Revisiting Forecasting of Recessions via Dynamic Probit for Time Series by Kauppi and Saikkonen (2008)," CEPA Working Papers Series WP032017, School of Economics, University of Queensland, Australia.
    8. Regmi, Krishna & Henderson, Daniel J., 2019. "Labor Demand Shocks at Birth and Cognitive Achievement during Childhood," IZA Discussion Papers 12521, Institute of Labor Economics (IZA).
    9. Arnerić Josip, 2020. "Realized density estimation using intraday prices," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 1-9, May.
    10. Chu, Chi-Yang & Henderson, Daniel J. & Parmeter, Christopher F., 2017. "On discrete Epanechnikov kernel functions," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 79-105.

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