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A simple root n bandwidth selector for nonparametric regression

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  • Heiler, Siegfried
  • Feng, Yuanhua

Abstract

The problem of selecting bandwidth for nonparametric regression is investigated. The methodology used here is a double-smoothing procedure with data-driven pilot bandwidths. After giving an extension of the asymptotic result of Hardle, Hall and Marron (1992) by transfering the ideas of Jones, Marron and Park (1991) into the context of nonparametric regression, some fast data-driven bandwidth selectors for nonparametric regression are proposed. One of them, hpsi, is root n consistent. The performance of these bandwidth selectors is studied through simulation for local linear regression. They are also compared with the bandwidth selected by R criterion and the true ASE optimal bandwidth (HASE). Though all of them show a satisfactory performance, the root n bandwidth selector turns out to be the best.

Suggested Citation

  • Heiler, Siegfried & Feng, Yuanhua, 1995. "A simple root n bandwidth selector for nonparametric regression," Discussion Papers, Series II 286, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    • Handle: RePEc:zbw:kondp2:286
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    References listed on IDEAS

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    1. Cleveland, William S. & Devlin, Susan J. & Grosse, Eric, 1988. "Regression by local fitting : Methods, properties, and computational algorithms," Journal of Econometrics, Elsevier, vol. 37(1), pages 87-114, January.
    2. Cao, R., 1993. "Bootstrapping the Mean Integrated Squared Error," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 137-160, April.
    3. Heiler, Siegfried & Feng, Yuanhua, 1995. "Data-driven optimal decomposition of time series," Discussion Papers, Series II 287, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    4. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
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    Cited by:

    1. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    2. Heiler, Siegfried & Feng, Yuanhua, 1997. "A bootstrap bandwidth selector for local polynomial fitting," Discussion Papers, Series II 344, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    3. Heiler, Siegfried & Feng, Yuanhua, 1995. "Data-driven optimal decomposition of time series," Discussion Papers, Series II 287, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    4. Yuanhua Feng, 2013. "Double-conditional smoothing of high-frequency volatility surface in a spatial multiplicative component GARCH with random effects," Working Papers CIE 65, Paderborn University, CIE Center for International Economics.

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