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The Stata Journal
Volume 10 Number 2: pp. 165-199

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Resampling variance estimation for complex survey data

Stanislav Kolenikov
University of Missouri
Columbia, MO
[email protected]
Abstract.  In this article, I discuss the main approaches to resampling variance estimation in complex survey data: balanced repeated replication, the jackknife, and the bootstrap. Balanced repeated replication and the jackknife are implemented in the Stata svy suite. The bootstrap for complex survey data is implemented by the bsweights command. I describe this command and provide working examples.

Editors’ note. This article was submitted and accepted before the new svy bootstrap prefix was made available in the Stata 11.1 update. The variance estimation method implemented in the new svy bootstrap prefix is equivalent to the one in bs4rw. The only real difference is syntax. For example, 
  . bs4rw, rw(bw*): logistic highbp height weight age female [pw=finalwgt]
is equivalent to 
  . svyset [pw=finalwgt], vce(bootstrap) bsrweight(bw*)
  . svy: logistic highbp height weight age female
Similarly, the example using mean bootstrap replicate weights, 
  . local mean2fay = 1-sqrt(1/10)
  . svyset [pw=finalwgt], vce(brr) brrweight(bw*) fay(`mean2fay´)
  . svy: logistic highbp height weight age female
is equivalent to 
  . svyset [pw=finalwgt], vce(bootstrap) bsrweight(bw*) bsn(10)
  . svy: logistic highbp height weight age female
The weights created by the bsweights command discussed in this article are equally applicable with the bs4rw command and with the new vce(bootstrap) and bsrweight() options of svy and svyset.
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