A data-dependent shift of the bootstrap parameter space removes boundary-induced randomness from the limiting distribution, yielding valid bootstrap inference in predictive regressions under non-stationarity of the predictor.
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Parameters on the boundary in predictive regression
A data-dependent shift of the bootstrap parameter space removes boundary-induced randomness from the limiting distribution, yielding valid bootstrap inference in predictive regressions under non-stationarity of the predictor.