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Constructing narrowest pathwise bootstrap prediction bands using threshold accepting
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DOI：10.1016/j.ijforecast.2012.09.004.png)
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Typically, 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. (C) 2012 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Bootstrapping
Forecast path
Prediction bands
Threshold accepting
Vector autoregressive models
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