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RE: st: bootstrap weights
From
Cameron McIntosh <[email protected]>
To
STATA LIST <[email protected]>
Subject
RE: st: bootstrap weights
Date
Fri, 25 Nov 2011 16:32:25 -0500
Stefano,
I wonder if another, conservative approach might be to form clusters that maximize ICC, or more directly the intra-cluster covariances of the parameter estimates, and then use Taylor linearization to get your adjusted standard errors. It may take a bit of work to write an algorithm to generate clusters such as I describe.
For what it's worth,
Cam
> Date: Fri, 25 Nov 2011 19:39:43 +0000
> Subject: Re: st: bootstrap weights
> From: [email protected]
> To: [email protected]
>
> Dear Bryan and Steven,
>
> thank you very much for your help.
>
> Only today the national statistics office confirmed to me that: 1) no
> clusters were used in the household survey I have, and 2) they cannot
> release information about strata.
> Thus, my microdata set only includes sampling weights (which sum up to
> Ireland's household population, N).
>
> Given this, what is the best I can do? I have thought of the following:
>
> svyset _n [pw = my_sampling_weights]
> bsweights bsw, n(0) reps(100)
> bs4rw exp_list, rw(bsw*): command [pw = my_sampling_weights]
>
> The first line - on Steven's suggestion - states that there is a
> single stratum only and each household is a PSU.
> The second line creates bootstrap resampling weights, which are then
> used in the bootstrap of exp_list (-bsweights- and -bs4rw- are
> commands written by Stas Kolenikov).
>
> I'm aware that not having information on strata affects results.
> However, my two questions are:
>
> 1) Do sampling weights mitigate this error?
>
> 2) Are the three command lines above the best I can do, given the
> information limitations?
>
> Once again I'd greatly appreciate your help.
>
> Best regards,
> Stefano Verde
>
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