Why do you need block bootstrap for time series?

hard

Answer

$Standard bootstrap resamples individual points, destroying temporal dependence and grossly under-estimating variance for autocorrelated data.$
$Block bootstrap resamples contiguous blocks (moving or stationary) to preserve short-range dependence.$
$(\mathrm{rule}\;\mathrm{of}\;\mathrm{thumb}\;n(1 / 3))$
$Foundational for CIs in time-series metrics, hedge-fund performance, drift detection.$

Check yourself — multiple choice

Same as regular
Standard bootstrap destroys temporal correlation → understates variance; block bootstrap resamples contiguous blocks $(\mathrm{length}\; \sim \;n(1 / 3))$ preserving short-range dependence
Only for regression
Not needed

Block bootstrap: preserves temporal correlation via contiguous blocks.

#bootstrap#confidence-interval

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