Sieve Bootstrap for Time Series Sieve Bootstrap for Time Series
نویسنده
چکیده
We study a bootstrap method which is based on the method of sieves. A linear process is approximated by a sequence of autoregressive processes of order p = pn, where pn ! 1 ; p n = on as the sample size n ! 1. F or given data, we t h e n estimate such a n A R pn model and generate a bootstrap sample by resampling from the residuals. This sieve bootstrap enjoys a nice nonparametric property. We show its consistency for a class of nonlinear estimators and compare the procedure with the blockwise bootstrap, which has been proposed by K unsch 1989. In particular, the sieve bootstrap variance of the mean is shown to have a better rate of convergence if the dependence between separated values of the underlying process decreases suuciently fast with growing separation. Finally a simulation study helps illustrating the advantages and disadvantages of the sieve compared to the blockwise bootstrap.
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