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Re:st: margins, vce(unconditional) after estimation with replicate weights
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re:st: margins, vce(unconditional) after estimation with replicate weights
Date
Tue, 6 Nov 2012 14:01:37 -0500
Sam Schulhofer-Wohl <[email protected]> wrote on Nov. 5:
I get a syntax error message, r(459), when I try to use -margins,
vce(unconditional)- after estimating a linear regression in survey data
with SDR or BRR weights.
The manual entry for -margins- (p. 1077, Version 12) states that
vce(unconditional) uses the linearized variance matrix, so, by
implication, can't be used directly with replicate-based standard
errors. I forwarded Sam's post to Stata Technical support. Isabelle
Canette of StataCorp, responded and demonstrated how to write a wrapper
program to feed into -svy: brr-, -svy: sdr-, or -svy: jackknife-.
Steve
The option -vce(unconditional)- for -margins- uses the linearized
variance estimator to compute the standard errors. Therefore, it can't
be used with -svy brr-.
If you want to compute standard errors for your margins based on
balanced repeated replications, you can write a wrapper for your
command and for -margins- and call it with -svy brr-, for example:
use nhanes2brr, clear
capture program drop mymargins
program mymargins, eclass
version 12
syntax [anything] [if] [iw pw]
if "`weight'" != "" {
local wgtexp "[`weight' `exp']"
}
set buildfvinfo on
logit highbp weight i.region `if' `wgtexp'
margins region, post
end
webuse nhanes2brr, clear
svy brr _b: mymargins
Notice that the program (-mymargins-) needs to accept weights.
Also, the line
set buildfvinfo on
is set so -margins- checks for estimability. It is usually set
on, but when using replication methods is set off because it
increases the computation time, so you need to set it on.
The option -post- for -margins- posts the results on e(b), so
they can be used by -svy brr-.
-----Begin Original Message-----
Re: st: margins, vce(unconditional) after estimation with replicate weights
From: Sam Schulhofer-Wohl <[email protected]>
I get a syntax error message, r(459), when I try to use -margins,
vce(unconditional)- after estimating a linear regression in survey
data with SDR or BSR weights. There is no error when I don't specify
vce(unconditional) or when I don't use replicate weights. Some
examples are below.
I think this may be the same problem that Eva Chang mentioned at
http://www.stata.com/statalist/archive/2012-09/msg01007.html, but I
don't see any replies to Eva's message.
I also can't find any indication in the manual that -margins,
vce(unconditional)- is incompatible with replicate weights.
Any suggestions on how to avoid this error?
--
Sam Schulhofer-Wohl
Senior Economist
Research Department
Federal Reserve Bank of Minneapolis
90 Hennepin Ave.
Minneapolis MN 55480-0291
[email protected]
*example of the error using SDR weights
. webuse ss07ptx
. svyset
pweight: pwgtp
VCE: sdr
MSE: off
sdrweight: pwgtp1 pwgtp2 pwgtp3 pwgtp4 pwgtp5 pwgtp6 pwgtp7 pwgtp8
pwgtp9 pwgtp10 pwgtp11 pwgtp12 pwgtp13 pwgtp14 pwgtp15 pwgtp16 pwgtp17
pwgtp18 pwgtp19
pwgtp20 pwgtp21 pwgtp22 pwgtp23 pwgtp24 pwgtp25 pwgtp26
pwgtp27 pwgtp28 pwgtp29 pwgtp30 pwgtp31 pwgtp32 pwgtp33 pwgtp34
pwgtp35 pwgtp36 pwgtp37
pwgtp38 pwgtp39 pwgtp40 pwgtp41 pwgtp42 pwgtp43 pwgtp44
pwgtp45 pwgtp46 pwgtp47 pwgtp48 pwgtp49 pwgtp50 pwgtp51 pwgtp52
pwgtp53 pwgtp54 pwgtp55
pwgtp56 pwgtp57 pwgtp58 pwgtp59 pwgtp60 pwgtp61 pwgtp62
pwgtp63 pwgtp64 pwgtp65 pwgtp66 pwgtp67 pwgtp68 pwgtp69 pwgtp70
pwgtp71 pwgtp72 pwgtp73
pwgtp74 pwgtp75 pwgtp76 pwgtp77 pwgtp78 pwgtp79 pwgtp80
Single unit: missing
Strata 1: <one>
SU 1: <observations>
FPC 1: <zero>
. svy: reg agep i.sex
(running regress on estimation sample)
SDR replications (80)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
..............................
Survey: Linear regression Number of obs = 230817
Population size = 23904380
Replications = 80
Wald chi2(1) = 1515.50
Prob > chi2 = 0.0000
R-squared = 0.0021
------------------------------------------------------------------------------
| SDR
agep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
2.sex | 1.994219 .0512265 38.93 0.000 1.893817 2.094621
_cons | 33.24486 .0470986 705.86 0.000 33.15255 33.33717
------------------------------------------------------------------------------
. margins sex, vce(unconditional)
vce(sdr) is not supported
something that should be true of your data is not
r(459);
*margins works fine if I don't specify vce(unconditional)
. margins sex
Adjusted predictions Number of obs = 230817
Model VCE : SDR
Expression : Linear prediction, predict()
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sex |
1 | 33.24486 .0470986 705.86 0.000 33.15255 33.33717
2 | 35.23908 .0386398 911.99 0.000 35.16335 35.31481
------------------------------------------------------------------------------
*using the BSR equivalent of SDR doesn't help
. svyset [pw=pwgtp], bsrweight(pwgtp?*) bsn(4) vce(bootstrap)
pweight: pwgtp
VCE: bootstrap
MSE: off
bsrweight: pwgtp1 pwgtp2 pwgtp3 pwgtp4 pwgtp5 pwgtp6 pwgtp7 pwgtp8
pwgtp9 pwgtp10 pwgtp11 pwgtp12 pwgtp13 pwgtp14 pwgtp15 pwgtp16 pwgtp17
pwgtp18 pwgtp19
pwgtp20 pwgtp21 pwgtp22 pwgtp23 pwgtp24 pwgtp25 pwgtp26
pwgtp27 pwgtp28 pwgtp29 pwgtp30 pwgtp31 pwgtp32 pwgtp33 pwgtp34
pwgtp35 pwgtp36 pwgtp37
pwgtp38 pwgtp39 pwgtp40 pwgtp41 pwgtp42 pwgtp43 pwgtp44
pwgtp45 pwgtp46 pwgtp47 pwgtp48 pwgtp49 pwgtp50 pwgtp51 pwgtp52
pwgtp53 pwgtp54 pwgtp55
pwgtp56 pwgtp57 pwgtp58 pwgtp59 pwgtp60 pwgtp61 pwgtp62
pwgtp63 pwgtp64 pwgtp65 pwgtp66 pwgtp67 pwgtp68 pwgtp69 pwgtp70
pwgtp71 pwgtp72 pwgtp73
pwgtp74 pwgtp75 pwgtp76 pwgtp77 pwgtp78 pwgtp79 pwgtp80
bsn: 4
Single unit: missing
Strata 1: <one>
SU 1: <observations>
FPC 1: <zero>
. svy: reg agep i.sex
(running regress on estimation sample)
Bootstrap replications (80)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
..............................
Survey: Linear regression Number of obs = 230817
Population size = 23904380
Replications = 80
Wald chi2(1) = 1515.50
Prob > chi2 = 0.0000
R-squared = 0.0021
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
agep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
2.sex | 1.994219 .0512265 38.93 0.000 1.893817 2.094621
_cons | 33.24486 .0470986 705.86 0.000 33.15255 33.33717
------------------------------------------------------------------------------
. margins sex, vce(unconditional)
vce(bootstrap) is not supported
something that should be true of your data is not
r(459);
*no problem if I don't use replicate weights
. svyset [pw=pwgtp]
pweight: pwgtp
VCE: linearized
Single unit: missing
Strata 1: <one>
SU 1: <observations>
FPC 1: <zero>
. svy: reg agep i.sex
(running regress on estimation sample)
Survey: Linear regression
Number of strata = 1 Number of obs = 230817
Number of PSUs = 230817 Population size = 23904380
Design df = 230816
F( 1, 230816) = 350.93
Prob > F = 0.0000
R-squared = 0.0021
------------------------------------------------------------------------------
| Linearized
agep | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
2.sex | 1.994219 .1064544 18.73 0.000 1.785571 2.202867
_cons | 33.24486 .0730543 455.07 0.000 33.10168 33.38805
------------------------------------------------------------------------------
. margins sex, vce(unconditional)
Adjusted predictions Number of obs = 230817
Expression : Linear prediction, predict()
------------------------------------------------------------------------------
| Linearized
| Margin Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sex |
1 | 33.24486 .0730543 455.07 0.000 33.10168 33.38805
2 | 35.23908 .0774313 455.10 0.000 35.08732 35.39084
------------------------------------------------------------------------------
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-----End Original Message-----
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