It is possible to allow for correlations across units by using block-bootstrap.Basic bootstrapping assumes observations are independent of each other.
It assumes your original model is correctly specified, for example. Although it feels entirely data-driven, bootstrap standard errors rely on assumptions just like everything else.
This approach is generally used in cases where calculating the analytical standard error of a statistic would be too difficult or impossible. So the standard deviation of the statistic across bootstrap samples can be used as an estimate of standard error. If you produce many bootstrap samples and calculate a statistic in each of them, then under certain conditions, the distribution of that statistic across the bootstrap samples is the sampling distribution of that statistic. In fact, each observation has the same probability of being selected for each bootstrap sample.īootstrap is commonly used to calculate standard errors. If you have a data set of size \(N\), then (in its simplest form) a “bootstrap sample” is a data set that randomly selects \(N\) rows from the original data, perhaps taking the same row multiple times. This site uses Just the Docs, a documentation theme for Jekyll.īoostrapping is a statistical method that uses random sampling with replacement to determine the sampling variation of an estimate.
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