Stationary bootstrapping for non-parametric estimator of nonlinear autoregressive model

Eunju Hwang, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We consider stationary bootstrap approximation of the non-parametric kernel estimator in a general kth-order nonlinear autoregressive model under the conditions ensuring that the nonlinear autoregressive process is a geometrically Harris ergodic stationary Markov process. We show that the stationary bootstrap procedure properly estimates the distribution of the non-parametric kernel estimator. A simulation study is provided to illustrate the theory and to construct confidence intervals, which compares the proposed method favorably with some other bootstrap methods.

Original languageEnglish
Pages (from-to)292-303
Number of pages12
JournalJournal of Time Series Analysis
Issue number3
StatePublished - May 2011


  • Non-parametric kernel estimator
  • Nonlinear autoregressive process
  • Primary: 62G08, 62M05
  • Secondary: 62F40
  • Stationary bootstrap procedure


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