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Forecasting time series with sieve bootstrap.

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URI: https://hdl.handle.net/10016/14838
ISSN: 0378-3758
DOI: 10.1016/S0378-3758(01)00092-1
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forecasting_JSPI_2002_ps.pdf (110.43 KB)
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2002-01-01
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Elsevier
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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.
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Sieve bootstrap, Prediction intervals, Time series, Linear processes
Bibliographic citation
Journal of Statistical Planning and Inference, (1 Jan. 2002), 100(1), 1-11.
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