Constructing narrowest pathwise bootstrap prediction bands using threshold accepting
AbstractTypically, prediction bands for path-forecasts are constructed pointwise, while inference relates to the whole forecasted path. In general, no closed form analytical solution is available for pathwise bands in finite samples. We consider a direct construction approach based on bootstrapped prediction bands. The resulting highly complex optimization problem is tackled using the local search heuristic of threshold accepting. A comparison with pointwise and asymptotic bands is provided, demonstrating superior properties of the proposed bands in small samples. Finally, a real application shows the practical implications of using an appropriate tool for generating the prediction bands.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 29 (2013)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Bootstrapping; Forecast path; Prediction bands; Threshold accepting; Vector autoregressive models;
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