Valid Resampling of Higher-Order Statistics Using the Linear Process Bootstrap and Autoregressive Sieve Bootstrap |
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Authors: | Carsten Jentsch Dimitris N Politis |
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Institution: | 1. Department of Economics , University of Mannheim , Mannheim , Germany cjentsch@mail.uni-mannheim.de;3. Department of Mathematics , University of California , San Diego, La Jolla , California , USA |
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Abstract: | We show that the linear process bootstrap (LPB) and the autoregressive sieve bootstrap (AR sieve) are, in general, not valid for statistics whose large-sample distribution depends on moments of order higher than two, irrespective of whether the data come from a linear time series or not. Inspired by the block-of-blocks bootstrap, we circumvent this non-validity by applying the LPB and AR sieve to suitably blocked data and not to the original data itself. In a simulation study, we compare the LPB, AR sieve, and moving block bootstrap applied directly and to blocked data. |
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Keywords: | AR sieve bootstrap Block bootstrap Generalized means Linear process bootstrap Resampling of blocks Sample autocovariances |
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