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Bahadur Representation for the Nonparametric M-Estimator Under Alpha-mixing Dependence

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Author Info
Yebin Cheng () (Faculty of Economics and Econometrics, Universiteit van Amsterdam)
Jan G. de Gooijer () (Faculty of Economics and Econometrics, Universiteit van Amsterdam)

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Abstract

Under the condition that the observations, which come from a high-dimensional population (X,Y), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric M-estimator for the unknown function m(x)=arg minaIE(r(a,Y)|X=x), where the loss function r(a,y) is measurable.
Furthermore, some related simulations are illustrated by using the cross validation method for both bivariate linear and bivariate nonlinear time series contaminated by heavy-tailed errors. The M-estimator is applied to a series of S&P 500 index futures andspot prices to compare its performance in practice with the "usual" squared-loss regression estimator.

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Publisher Info
Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 05-067/4.

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Date of creation: 21 Jun 2005
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Handle: RePEc:dgr:uvatin:20050067

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Related research
Keywords: Asymptotic representation; Kernel function; Robust estimator; Strongly-mixing;

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

References listed on IDEAS
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  1. Cai, Zongwu & Ould-Saïd, Elias, 2003. "Local M-estimator for nonparametric time series," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 433-449, December. [Downloadable!] (restricted)
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