RE: st: Marginal Effect

["s_azagba@live.concordia.ca" <s_azagba@live.concordia.ca>]

Hi All,

I estimated a random effect probit model using xtprobit in  stata 11.
 Marginal effects using margins, dydx(*) predict(pu0)  assumes u_i =0 which l think implies rho =  0 since rho = 0.
 Please is there any command that l can use to change this  assumption?

Pls any other suggestion on what to do


_______________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of s_azagba@live.concordia.ca [s_azagba@live.concordia.ca]
Sent: Wednesday, January 05, 2011 3:36 PM
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: Marginal Effect

Hi Marten,
I thought if the unobserved individual effect i. e u_i = 0 implies that rho which is the relative importance of the unobserved effect  is 0. I think it is same as the neglected heterogeneity in the estimation of partial effect  Wooldridge was refering to in his book on chapter 15.


________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Maarten buis [maartenbuis@yahoo.co.uk]
Sent: Wednesday, January 05, 2011 1:46 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Marginal Effect

--- On Wed, 5/1/11, sazas  wrote:
> I estimated a random effect probit model using xtprobit in
> stata 11.
>
> Marginal effects using margins, dydx(*) predict(pu0)
> assumes u_i =0 which l think implies rho =  0 since rho = 0.
> Please is there any command that l can use to change this
> assumption?

It is not true that rho (proportion of variance due to group
level variance) is assumed to be 0. The way to think about it
is that there is an (unobserved) group level variable added
to your model and that you compute your marginal effects for
individuals that have an average value on this variable. I do
not think that is a too problematic assumption, but there is
always a bit of friction when using marginal effects for this
type of models. In essence you are trying to fit a linear
line to a non-linear one, which will often (but not always)
produce an ok summary of the non-linear line, but it will
never be exactly right. If you are a purist, then you should
probably use -xtlogit- and interpret the odds ratios rather
than the marginal effects. In practice, I would use which ever
model and effect size I prefer, and look at tables of
predicted probabilities and odds, look at graphs of predicted
probablities and odds and look if I can (approximately) match
those with the effect sizes I found. If you can do that, then
there isn't much of a problem.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------




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