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Re: st: margeff and margins puzzle
From
Patrick Roland <[email protected]>
To
[email protected]
Subject
Re: st: margeff and margins puzzle
Date
Fri, 29 Jul 2011 09:36:50 -0700
This is very odd. I am using Stata 11.2 and my version of margeff is up to date:
. which margeff
*! Obtain partial effects after estimation
*! Version 2.2.0 (20 August 2009) (Revision of Stata Journal submission)
*! Author: Tamas Bartus (Corvinus University, Budapest)
The results I get from margins are the same as you, Richard. The
results I get from margeff are:
. margeff
Average partial effects after probit
y = Pr(y)
----------------------------------------------------------------------------------------------------
variable | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 0 .0518072 0.00 1.000 -.1015403 .1015403
----------------------------------------------------------------------------------------------------
On Thu, Jul 28, 2011 at 7:11 PM, Richard Williams
<[email protected]> wrote:
> At 06:35 PM 7/28/2011, Patrick Roland wrote:
>>
>> Hi all,
>>
>> Here's a piece of code which gives peculiar results. It's a simple probit.
>> "margeff" calculates the marginal effect to be exactly zero, whereas
>> margins
>> (correctly) doesn't. The really puzzling thing is that if "set obs 1000"
>> is
>> changed to "set obs 100", they give identical results, as they should!
>> Anyone have any idea what might be going on here?
>>
>>
>> set seed 1
>> set obs 1000
>> gen x0 = -.25
>> gen x1 = runiform()
>> gen eps = rnormal()
>> gen y = x0+x1+eps > 0
>> probit y x1
>> margeff
>> margins, dydx(*)
>
> I get identical results with both commands. As always, make sure you have
> the latest versions of everything and try again.
>
> . set seed 1
>
> . set obs 1000
> obs was 0, now 1000
>
> . gen x0 = -.25
>
> . gen x1 = runiform()
>
> . gen eps = rnormal()
>
> . gen y = x0+x1+eps > 0
>
> . probit y x1
>
> Iteration 0: log likelihood = -669.63431
> Iteration 1: log likelihood = -637.00519
> Iteration 2: log likelihood = -636.94994
> Iteration 3: log likelihood = -636.94994
>
> Probit regression Number of obs =
> 1000
> LR chi2(1) =
> 65.37
> Prob > chi2 =
> 0.0000
> Log likelihood = -636.94994 Pseudo R2 =
> 0.0488
>
> ------------------------------------------------------------------------------
> y | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
> x1 | 1.133848 .1423649 7.96 0.000 .8548182
> 1.412878
> _cons | -.2788769 .0797004 -3.50 0.000 -.4350869
> -.122667
> ------------------------------------------------------------------------------
>
> . margeff
>
> Average partial effects after probit
> y = Pr(y)
>
> ------------------------------------------------------------------------------
> variable | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
> x1 | .4126165 .0466284 8.85 0.000 .3212265
> .5040065
> ------------------------------------------------------------------------------
>
> .
> . margins, dydx(*)
>
> Average marginal effects Number of obs =
> 1000
> Model VCE : OIM
>
> Expression : Pr(y), predict()
> dy/dx w.r.t. : x1
>
> ------------------------------------------------------------------------------
> | Delta-method
> | dy/dx Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
> x1 | .4126127 .0466285 8.85 0.000 .3212225
> .5040028
> ------------------------------------------------------------------------------
>
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: [email protected]
> WWW: http://www.nd.edu/~rwilliam
>
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