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Re: AW: st: RE: wald tests with mfx
No, we are comparing like with like. So when we evaluate our typical
person who received an inheritance, we evaluate how earned income,
inherited income, etc. affect giving by people at the mean of the
subsample receiving an inheritance. It is only within this group that
we test equality of the coefficient on earned income and inherited
income. Then we do it for the typical person receiving earned income,
evaluated at the "earned income receiving" mean and testing for equality
across income coefficients here. We never compare the marginal
propensity to give out of inheritance for the typical person receiving
an inheritance with the marginal propensity to give out of inheritance
for the typical person with earned income, for the reason you talk about.
I still haven't gotten a response on an elegant way to do what we need
in stata, where we use a single tobit to produce all these subsample
marginal effects and do the appropriate within equality tests, and the
problem is that all the postestimation stuff refers to the tobit, and
not the changes on that tobit resulting from our mfx calculation. But I
did follow up on the alternative where the problem doesn't arise (just
estimate a separate tobit for each subsample) and got really interesting
results except for the smallest subsample (those receiving welfare
payments).
Maarten buis wrote:
--- On Mon, 13/7/09, Rich Steinberg wrote:
What I want is the vce of the marginal effects at the mean
of various subsamples of the data. I had thought, like
you, that if two marginal effects are statistically
different in the tobit evaluated at the mean of the full
sample, the same would be true when evaluated for any
subsample. But when I run mfx on various subsamples,
the Z scores of each coefficient are sensitive to the
subsample. More is going on than simply applying the
McDonald and Moffitt decomposition to a different level of
censorship when I run that mfx command. Maybe that is
a bug, but my more sophisticated friend thinks that mfx at a
subsample mean is computing correctly and I am
misunderstanding how to apply it to subsamples. Thus
my current query.
Martin, and anyone else who is curious, here is the
research problem that generated this strategy. We are
seeing whether the marginal propensity to make charitable
donations differs with the source of income (or annuitized
equivalent for wealth variables), among other things,
testing the implicit assumption of economists that money is
money and consumption behavior of individuals does not
depend on where that money came from. So for example,
we want to know if the marginal propensity to give out of
welfare transfer payments equals that out of earned income,
annuitized inheritance income, etc. A single tobit
evaluation tells us whether the coefficients on these
various income measures are equal around the mean of our
full sample. But there are no observations where the
respondent received welfare income and had the mean level of
other income sources, so the comparison at the mean has no
clear interpretation of the sort we want. In our base
model, we therefore test for the equality of coefficients
within each of 7 subsample means (defined by having positive
levels of each of our 7 income variables). (We hope to
have more advanced estimates accounting for unmeasured
heterogeneity that makes a person receiving welfare
different from a person receiving an inheritance, but we are
not there yet). Maybe we should estimate the entire
tobit separately for each subsample, but as some of the
subsamples are small (particularly our main income source of
interest, inheritances) we were hoping those regressions
could be pooled because the slopes with respect to the
latent dependent variable were not significantly different
in the subsamples.
The marginal effect depends on where you evaluate it. More,
specifically it will become less when you evaluate at a point
closer to the cut-off point. So if you evaluate the marginal
effects of different groups at different means you will add a
source of variation in results between these groups that you
may not want, because now you are not comparing like with like.
I can think of two suggestions:
1) Maybe the overall median lies within the range of all
sub-groups. In that case you could avoid the entire problem by
estimating the marginal effects at this value.
2) You could compare the effects on the latent variable, as
these effects are linear, and thus not depend on where you
evaluate them.
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
Martin Weiss wrote:
<>
You are more than welcome! I did not endorse your
research strategy as I do
not think that what you are attempting is necessary,
but on a technical
level, you can get your hands on the desired returned
result in this
fashion.
Stata provides a full array of postestimation tools
for every estimation
command, and this list is usually exhaustive. If you
want the -vce- of the
original estimation, you can get it like this:
***
sysuse auto, clear
generate wgt=weight/1000
tobit mpg wgt gear_ratio, ll(17)
estat vce
***
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]]
Im Auftrag von Rich Steinberg
Gesendet: Montag, 13. Juli 2009 22:51
An: [email protected]
Betreff: Re: st: RE: wald tests with mfx
Thanks for responding, and so quickly. Good
answer, but I learned from following your advice that this
reproduces only the standard errors, not the covariances I
would need for a Wald test. I don't see an e( ) that
saves the vce. And I can't use the vce from the
original tobit because the sample is changing.
Martin Weiss wrote:
<>
" For the latter, I know from the Help file that
mfx saves what I need as e(Xmfx_se_dydx), but I can't figure
out how to see that."
***
sysuse auto, clear
generate wgt = weight/100
tobit mpg wgt len tu head, /*
*/ ll(17) ul(24)
mfx compute, /*
*/ predict(e(17,24))
mat l e(Xmfx_se_dydx)
mat A= e(Xmfx_se_dydx)
matrix list A
di A[1,3]
***
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]]
On Behalf Of Rich Steinberg
Sent: Montag, 13. Juli 2009 22:10
To: [email protected]
Subject: st: wald tests with mfx
As a relatively unsophisticated user, I have tried
for a few hours and failed to solve the following
problem. After running tobit, I want to test for the
equality of marginal effects for the unconditional
observable dependent variable. No problem with mfx or
dtobit. But I don't want to evaluate these marginal
effects test at the mean, median or zero of the full sample
prior to testing. Instead, I want to use the estimates
from the full sample, but evaluate the marginal effect at
means of various subsamples. So I have, for example:
tobit totgiv $income $control, ll vce(cluster
fid68)
mfx if welfare01>0, pred(ystar(0,.))
Now, I want to test for the equality of two
elements of $income in this subsample. But everything
I try works on the tobit coefficients, not the mfx
output. So how do I retrieve this for a "test" command
(ideally) or even display the vce from the mfx to do the
test by hand?
For the latter, I know from the Help file that mfx
saves what I need as e(Xmfx_se_dydx), but I can't figure out
how to see that. This should be easy, but stumped me.
Thanks everyone.
*
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-- Rich Steinberg
Department of Economics, 516 Cavanaugh Hall
IUPUI
Indianapolis, IN 46202-5140
317-278-7221
"Unanswered email since 1955"
*
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* http://www.stata.com/support/statalist/faq
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*
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--
Rich Steinberg
Department of Economics, 516 Cavanaugh Hall
IUPUI
Indianapolis, IN 46202-5140
317-278-7221
"Unanswered email since 1955"
*
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* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/