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Re: st: mlogit vs mprobit
Thanks for yoyr answer.
Yes, I used exactly the same data. I did:
xi: mlogit outc nb_in nb_app share_no_x share_no_y_ fwd d_pr_app
succes_mean stock_pim_mean nb_ds i.ap_year i.area2 df*, base(0)
xi: mprobit outc nb_in nb_app share_no_x share_no_y_ fwd d_pr_app
succes_mean stock_pim_mean nb_ds i.ap_year i.area2 df*, base(0) tech(bfgs)
and then
mfx, predict(outcome(0))
...etc
Note that I used the bfgs algorithm (insteas of the default "nr"). I also
tried to use the "dfp" algorithm which gives the same results. The reason is
that the "nr" algorith makes 1 iteration every half an hour and the other
techniques are much faster, but I don't think that the difference comes from
there.
There are 4 categories which differe a lot in terms of size:
outcome 0: 294 obs.
outcome 1: 3277
outcome 2: 176
outcome 3: 2674
As I wrote in the previous message, the marginal effects are not significant
when I use mlogit (i.e all the P>|z| values are equal to 1 or very close to
1) whereas some of the coefficiants of the marginal effects are significant
with mprobit
Andr�
From: Richard Williams <[email protected]>
Reply-To: [email protected]
To: [email protected]
Subject: Re: st: mlogit vs mprobit
Date: Fri, 24 Mar 2006 11:17:28 -0500
At 10:16 AM 3/24/2006, Andr� Paul wrote:
Dear all,
when I estimate successively a mlogit and a mprobit model, I get, as
expected roughly the same coefficients.
However, when I compute the marginal effects, the standard errors (of the
marginal effects) are much lower with mprobit. Actually, when I use
mlogit, none of the marginal effects are significant, whereas, most of
them become significant when I use mprobit.
Could someone give me the reason of this?
Thanks,
Andr�
I just tried an example, and both the marginal effects and standard errors
were very similar for both mlogit and mprobit. Are you sure something
wasn't different between the two runs, e.g. were the samples and variables
the same throughout? Did you use the -mfx- command in both cases, or a
different command? Are some of the categories extremely thin, are the
models having trouble converging? Perhaps you could post exactly what you
did.
As a sidelight, Tamas Bartus's -margeff- command will quickly estimate the
marginal effects after mlogit. Alas, it doesn't support mprobit. Note
that, by default, it estimates the marginal effects a little differently
than -mfx- does. If you want it to clone -mfx-'s behavior, give the
command
margeff, at(mean)
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
FAX: (574)288-4373
HOME: (574)289-5227
EMAIL: [email protected]
WWW (personal): http://www.nd.edu/~rwilliam
WWW (department): http://www.nd.edu/~soc
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