Re: st: Svy poststratification VS Pweighting

[Stas Kolenikov <skolenik@gmail.com>]

On Mon, Jun 21, 2010 at 11:42 AM, francesco manaresi <manaresi@gmail.com> wrote:
> I've seen several questions on the issue of poststratification in
> Statalist, but would like to ask you some clarifications on the
> estimate of standard errors. Thank you for your kindness and
> availability.
> I have got a sample of firms which have been (supposedly) randomly
> drawn from a reference population, and would like to post-stratify
> based on two observable characteristics for which all cross-tables are
> available.

The pweight has to be the inverse probability of selection, if
available. Period. If you have additional information on the
composition of the population, you should use post-stratification
capabilities. Using post-stratification adjustments to multiply the
original pweights and running anlaysis as if this composite weight
were the pure probability weight is a poor man's strategy, and should
be discouraged when more appropriate tools are available.

I would never trust a standard error of 5e-17 which is roughly
c(epsdouble). I don't know what you've done there, but you obviously
eliminated the variance in the sample, and you know this cannot be
right (unless you sampled all the units with probability of 1, at
which point it is not a random sample anymore).

As for matching, you are on your own there. I don't trust ANY standard
errors that come out of matching estimators, so they are all equally
bad, in my eyes. There is no clear population analogue of the matching
procedure for the finite population, so -svy:- mode of inference is
hardly applicable.

-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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