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st: Delta method std errors via "svy" vs. "robust" std errors via "glm"


From   "Ben M. Gramig" <gramigbe@msu.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Delta method std errors via "svy" vs. "robust" std errors via "glm"
Date   Wed, 9 Aug 2006 11:51:54 -0400

Can anyone clarify for me the difference in the standard errors generated by
(for instance) "svylogit" versus "glm [pweight], fam(bin) link(logit)
robust"?

The survey suite of commands indicate that std errors are calculated by
Taylor linearization (by default in Stata8, which I'm running), but it is
also my understanding (perhaps mistakenly?) that for non-linear regression
routines in Stata a Taylor series expansion is also used to calculate
"sandwich" std errors.

When I fit a model using both sets of commands for the same data I get very
similar std errors with no disagreement in inference from the two models.  

An example...

. svyset [pweight= bloodwt], strata( state)
pweight is bloodwt
strata is state

. svylogit  blv_pos west seast neast ttlcattle cleaninject_cow
cleaninject_heifer
> dehorn_safe dehorn_saw

[COMBINED OUTPUT BELOW]

. glm blv_pos west seast neast ttlcattle cleaninject_cow cleaninject_heifer
dehorn_safe 
> dehorn_saw [pweight=bloodwt], fam(bin) link(logit) robust



		LOGIT(glm)	SVYLOGIT
		Robust	
		Std. Err.	Std. Err.
		
West		0.3531389	0.3501034
seast		1.060446	1.090085
neast		0.2899766	0.2899524
ttlcattle	0.0007633	0.0007623
cleaninjec~w0.6201178	0.6199391
cleaninjec~r0.5917452	0.5901283
dehorn_safe	0.2837614	0.2842202
dehorn_saw	0.4650457	0.463464
_cons		0.2478754	0.2477516 


Note: I have a complex survey design but want to use glm to implement the
fractional logit model, which is not allowed in conjunction with svy.

Thanks,
Ben

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