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Optimal Smoothing for a Computationallyand StatisticallyEfficient Single Index Estimator

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  • Wolfgang Härdle
  • Oliver Linton
  • Yingcun Xia

Abstract

In semiparametric models it is a common approach to under-smooth thenonparametric functions in order that estimators of the finite dimensionalparameters can achieve root-n consistency. The requirement of under-smoothingmay result as we show from inefficient estimation methods or technical difficulties.Based on local linear kernel smoother, we propose an estimation method toestimate the single-index model without under-smoothing. Under some conditions,our estimator of the single-index is asymptotically normal and most efficient in thesemi-parametric sense. Moreover, we derive higher expansions for our estimatorand use them to define an optimal bandwidth for the purposes of index estimation.As a result we obtain a practically more relevant method and we show its superiorperformance in a variety of applications.

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Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2009/537.

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Date of creation: Jul 2009
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Handle: RePEc:cep:stiecm:/2009/537

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Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp

Related research

Keywords: ADE; Asymptotics; Bandwidth; MAVE method; Semiparametricefficiency.;

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  1. Hardle, Wolfgang & Tsybakov, A. B., 1993. "How sensitive are average derivatives?," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 31-48, July.
  2. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521424318.
  3. Xia, Yingcun, 2006. "Asymptotic Distributions For Two Estimators Of The Single-Index Model," Econometric Theory, Cambridge University Press, vol. 22(06), pages 1112-1137, December.
  4. Yoshihiko Nishiyama & Peter M. Robinson, 2005. "The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, vol. 73(3), pages 903-948, 05.
  5. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
  6. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  7. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
  8. Yingcun Xia & Howell Tong & W. K. Li & Li-Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410.
  9. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521370905.
  10. Powell, James L. & Stoker, Thomas M., 1996. "Optimal bandwidth choice for density-weighted averages," Journal of Econometrics, Elsevier, vol. 75(2), pages 291-316, December.
  11. Hardle, W. & Hall, P. & Ichimura, H., 1991. "Optimal smoothing in single index models," CORE Discussion Papers 1991007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. Y. Nishiyama & P. M. Robinson, 2000. "Edgeworth Expansions for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, vol. 68(4), pages 931-980, July.
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