Overlapping sub-sampling and invariance to initial conditions
This paper studies the use of the overlapping blocking scheme in unit root autoregression. When the underlying process is that of a random walk, the blocksâ€™ initial conditions are not fixed, but are equal to the sum of all the previous observationsâ€™ error terms. When non- overlapping subsamples are used, as first shown by Chambers and Kyriacou (2010), these initial conditions do not disappear asymptotically. In this paper we show that a simple way of overcoming this issue is to use overlapping blocks. By doing so, the effect of these initial conditions vanishes asymptotically. An application of these findings to jackknife estimators indicates that an estimator based on moving-blocks is able to provide obvious reductions to the mean square error
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