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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10016/14838
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| Title: | Forecasting time series with sieve bootstrap. |
| Author(s): | Alonso, Andrés M.
Peña, Daniel
Romo Urroz, Juan |
| Publisher: | Elsevier |
| Issued date: | 1-Jan-2002 |
| Citation: | Journal of Statistical Planning and Inference, (1 Jan. 2002), 100(1), 1-11. |
| URI: | http://hdl.handle.net/10016/14838 |
| ISSN: | 0378-3758 |
| DOI: | 10.1016/S0378-3758(01)00092-1 |
| Abstract: | In this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a general class of linear processes. Our approach uses the AR(∞)-sieve bootstrap procedure based on residual resampling from an autoregressive approximation to the given process. We present a Monte Carlo study comparing the finite sample properties of the sieve bootstrap with those of alternative methods. Finally, we illustrate the performance of the proposed method with a real data example. |
| Sponsor: | We would like to thank Mike Wiper, two referees and the coordinating editor for carefully reading that greatly improved the paper. This research was partially supported by the Dirección General de Educación Superior project DGES PB96-0111 and Cátedra de Calidad BBVA. |
| Publisher version: | http://dx.doi.org/10.1016/S0378-3758(01)00092-1 |
| Keywords: | Sieve bootstrap
Prediction intervals
Time series
Linear processes |
| Appears in Collections: | DES - Artículos de Revistas
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