Using the Bootstrap as an Aid in Choosing the Approximate Representation for Vector Time Series

In this article, a procedure is presented to use the bootstrap in choosing the best approximation in terms of forecasting performance for the equivalent state-space representation of a vector autoregressive model. It is found that the proposed procedure, which uses each approximant's forecasting performance, can enhance considerably an approach based simply on the estimated Hankel singular values.

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