Abstract: In recent years, several new parametric and nonparametric
bootstrap methods have been proposed for time series data. Which of
these methods should applied researchers use? We provide evidence that
for many applications in time series econometrics parametric methods
are more accurate, and we identify directions for future research on
improving nonparametric methods. We explicitly address the important,
but often neglected issue of model selection in bootstrapping. In
particular, we emphasize the advantages of the AIC over other lag
order selection criteria and the need to account for lag order
uncertainty in resampling. We also show that the block size plays an
important role in determining the success of the block bootstrap, and
we propose a data-based block size selection procedure.
Keywords: Bootstrap, ARMA, frequency domain, blocks
Full paper (3923 KB PDF)
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