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From this, the analyst could choose the number of digits to round off from each standard error estimate when reporting results, and increase B until multiple calculations return the same rounded value.2 In addition, the seed of  the random number generator can be set to duplicate the same bootstrap samples in later iterations. In the article, I identify some differences that make BCSE preferable to the CLRT method: (1) CLRT does not calculate a full covariance matrix of the parameter estimates, (2) CLRT is driven by a much different philosophy than the other methods I examine, and (3) CLRT is more difficult to implement than BCSE.

Example Stata Code

1 * Method 1

2 regress y x1 x2 x3, vce(bootstrap, rep(1000) cluster(cluster))

3 * Method 2

4 bootstrap, rep(1000) cluster(cluster): regress y x1 x2 x3

5 predict residuals, residuals

6 loneway residuals cluster

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