Bernoulli

Bootstrap of kernel smoothing in nonlinear time series

Jürgen Franke, Jens-Peter Kreiss, and Enno Mammen

Full-text: Open access

Abstract

Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time series. We show that the bootstrap can be used for estimating the distribution of kernel smoothers. This can be done by mimicking the stochastic nature of the whole process in the bootstrap resample or by generating a simple regression model. Consistency of these bootstrap procedures will be shown.

Article information

Source
Bernoulli, Volume 8, Number 1 (2002), 1-37.

Dates
First available in Project Euclid: 10 March 2004

Permanent link to this document
https://projecteuclid.org/euclid.bj/1078951087

Mathematical Reviews number (MathSciNet)
MR2002k:62112

Zentralblatt MATH identifier
1006.62038

Keywords
bandwidth selection bootstrap kernel estimates local polynomial estimates nonparametric heteroscedastic autoregression nonparametric time series

Citation

Franke, Jürgen; Kreiss, Jens-Peter; Mammen, Enno. Bootstrap of kernel smoothing in nonlinear time series. Bernoulli 8 (2002), no. 1, 1--37. https://projecteuclid.org/euclid.bj/1078951087


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