Bootstrap prediction intervals for power-transformed time series
AbstractIn this paper we propose a bootstrap resampling scheme to construct prediction intervals for future values of a variable after a linear ARIMA model has been fitted to a power transformation of it. The advantages over existing methods for computing prediction intervals of power transformed time series are that the proposed bootstrap intervals incorporate the variability due to parameter estimation, and do not rely on distributional assumptions neither on the original variable nor on the transformed one. We show the good behavior of the bootstrap approach versus alternative procedures by means of Monte Carlo experiments. Finally, the procedure is illustrated by analysing three real time series data sets.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 21 (2005)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Other versions of this item:
- Lorenzo Pascual & Juan Romo & Esther Ruiz, 2001. "Bootstrap Prediction Intervals For Power-Transformed Time Series," Statistics and Econometrics Working Papers ws010503, Universidad Carlos III, Departamento de Estadística y Econometría.
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