Bootstrap prediction intervals for linear , nonlinear and nonparametric autoregressions

@inproceedings{Pan2014BootstrapPI,
  title={Bootstrap prediction intervals for linear , nonlinear and nonparametric autoregressions},
  author={Lichuan Pan and Dimitris N. Politis},
  year={2014}
}
In order to construct prediction intervals without the cumbersome—and typically unjustifiable—assumption of Gaussianity, some form of resampling is necessary. The regression set-up has been well-studied in the literature but time series prediction faces additional difficulties. The paper at hand focuses on time series that can be modeled as linear, nonlinear or nonparametric autoregressions, and develops a coherent methodology for the construction of bootstrap prediction intervals. Forward and… CONTINUE READING
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