My data is a stratified (on geographical areas) crossectional survey
weighted for design and dropout. Within each strata is a simple random
sample of a few hundred observations. I need robust standard error because
data are not truly Poisson (but with robust standard error it works
according to litterature).
I intended to do analysis like this:
svyset [pweight=wgt] , strata(str)
svy: poisson y x1 x2 x3 , irr
Will it work?
/Tomas
Re: st: Robust SE and svy
Austin Nichols
till:
statalist
2009-01-27 16:08
Sänt av:
[email protected]
Svara till statalist
Tomas Lind <[email protected]>:
I would say rather that -svy- is the same as vce(cluster psu) plus
weighting plus FPC etc. if specified. A cluster-robust VCE is also
heteroskedasticity-robust, so you can think of vce(robust) as
"included in" or "a special case of" vce(cluster psu) in some sense,
and the two are the same if each cluster contains exactly one
observation (which is the case if you -svyset _n- or -svyset,srs- as
Steve implies below). In most applications, clustering is more
important than weighting which is more important than any FPC, but
weighting can change point estimates whereas clustering only affects
VCEs. I usually leave off any FPC because I am interested in
model-based inference about an abstract data-generating process, not
about describing the finite population from which the survey data was
drawn, but note that this is not quite right for a stratified
sample--google "korn graubard superpopulation inference" for some
relevant literature.
Do you in fact have a "simple random sampling cross-sectional study"
or are you using a survey with a cluster design? Either way, you may
want to specify the vce(cluster var) option to account for possible
clustering by geographical area etc.; if there is no clustering, the
vce option will have little effect (i.e. there is little harm is
incorrectly clustering, as long as you have many balanced clusters, so
that no cluster contains more than about 2% of the data).
On Tue, Jan 27, 2009 at 9:04 AM, Steven Samuels
<[email protected]> wrote:
> --
> Yes it does. The default for -svyset- is vce(linearized), which is the
same
> as vce(robust) in the non-survey setting.
>
> If you are doing a Poisson or other regression model, you should not use
> the fpc anyway. That is appropriate only for descriptive statistics
about
> the sampled population. A good reference for all this is: Lohr, S. L.
> (1999). Sampling: Design and Analysis. Pacific Grove, CA: Brooks Cole
> Publishing Company. If you will be doing much survey analysis, I also
> recommend that you purchase the Stata survey manual.
\>
> -Steve
>
> On Jan 27, 2009, at 3:30 AM, Tomas Lind wrote:
>>
>> Does svy imply that I have robust SE?
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