Abstract
When studying a time series, one of the main goals is the estimation of forecast confidence intervals based on an observed trajectory of the process. The traditional approach of finding prediction intervals for a linear time series assumes that the distribution of the error process is known. Thus, these prediction intervals could be adversely affected by departures from the true underlying distribution.
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Alonso, A.M., Peña, D., Romo, J. (2000). Sieve bootstrap prediction intervals. In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_17
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DOI: https://doi.org/10.1007/978-3-642-57678-2_17
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1326-5
Online ISBN: 978-3-642-57678-2
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