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# Re: st: How to store marginal effect value after using margin command?

 From Steve Samuels <[email protected]> To [email protected] Subject Re: st: How to store marginal effect value after using margin command? Date Mon, 10 Jun 2013 10:14:10 -0400

``` Vũ Võ :

If you've now studied the -help- for -margins-, you'll know that as with
-mfx-, the marginal effects are contained in the returned matrix r(b)
and, with the post option, in e(b), as Kit stated. You can extract
values from these matrices, e.g.:

. scalar m1 = el(r(b),1,1)

But you really cannot do much with scalars; they are just numbers
unconnected to the command that generated them. The real advantage of
the post option is that it makes the estimated effects available as
Stata system "underscore variables" (type "help system variables"). To
find the names of those variables, you run a second -margins- command
with the "coeflegend" option, immediately after the first.

. sysuse auto, clear
. gen wtk = weight/1000
. reg price  rep78##c.wtk
. margins, dydx(wtk rep78)  post
. margins, coeflegend
. nlcom _b[wtk] + _b[2.rep78]/_b[3.rep78]  //silly manipulation
. nlcom _b[w]  + _b[2.r]/_b[3.r]  // abbreviate names

Your original question was whether -margins- could show the partial
derivatives that you had derived "by hand". The answer is "Yes". These
partial derivatives are contained in r(Jacobian) and e(Jacobian):

. reg price  c.wtk##c.wtk
. margins, dydx(wtk) at(wtk =(1(1)4))
. matrix list r(Jacobian)

For a constructive use of Jacobians, see Jeff Pitblado's post at:
http://www.stata.com/statalist/archive/2010-04/msg00860.html

Steve

> On Jun 8, 2013, at 8:20 PM, William Buchanan wrote:
>
> If you're using the - margins - command, the - post - option is clearly documented in the help file.  Additionally, the Statalist FAQ suggests looking through the documentation as a good first step prior to querying the list.  If this isn't the answer that you were looking for it may be a good idea to be more explicit about your end goal.
>
> HTH,
> Billy
>

Sent from my iPhone

On Jun 8, 2013, at 16:54, Vũ Võ <[email protected]> wrote:

> Dear Sergiy,
>
> Thank you for your message. Probably, you misunderstand my question
> nor I am clear in raising a question.
>
> For example:
>
> sysuse auto, clear
>
> reg price price wgt mpg c.wgt#c.wgt c.mpg#c.mpg c.wgt#c.mpg
>
>
>     Source |       SS       df       MS              Number of obs =      74
> -------------+------------------------------           F(  5,    68) =   12.84
>      Model |   308384833     5  61676966.6           Prob > F      =  0.0000
>   Residual |   326680563    68  4804125.93           R-squared     =  0.4856
> -------------+------------------------------           Adj R-squared =  0.4478
>      Total |   635065396    73  8699525.97           Root MSE      =  2191.8
>
> ------------------------------------------------------------------------------
>      price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>     weight |  -31.88985   9.148215    -3.49   0.001    -50.14483   -13.63487
>        mpg |  -3549.495   1126.464    -3.15   0.002    -5797.318   -1301.672
>            |
>   c.weight#|
>   c.weight |   .0034574   .0008629     4.01   0.000     .0017355    .0051792
>            |
> c.mpg#c.mpg |   38.74472   12.62339     3.07   0.003     13.55514    63.93431
>            |
>   c.weight#|
>      c.mpg |   .5421927   .1971854     2.75   0.008     .1487154    .9356701
>            |
>      _cons |   92690.55   25520.53     3.63   0.001     41765.12      143616
> ------------------------------------------------------------------------------
>
> So, I would like to calculate the marginal effects of weight and mpg
> on price by using command:
> margins, dydx(weight mpg)
>
> and get ->
>
> Average marginal effects                          Number of obs   =         74
> Model VCE    : OLS
>
> Expression   : Linear prediction, predict()
> dy/dx w.r.t. : weight mpg
>
> ------------------------------------------------------------------------------
>            |            Delta-method
>            |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>     weight |   .5360966   .6516553     0.82   0.411    -.7411243    1.813318
>        mpg |  -262.0502    103.681    -2.53   0.011    -465.2612   -58.83919
> ------------------------------------------------------------------------------
>
>
> My question is how to obtain or store in to scalar or whatever in
> stata values of 0.5360966 and -262.0502 in the above table?
>
> If i use mfx , after that using matrix list e(b) as you suggested, it
> will give me coefficients of the regression. To some extent, it can
> say that these are marginal effets, but not what I would like to
> obtain.
>
> Any suggestion are highly appreciated. Thanks
>
> Vu
>
> On 9 June 2013 00:07, Sergiy Radyakin <[email protected]> wrote:
>> type either 'return list' or 'ereturn list' (depending on the command)
>> to see which results are saved after these commands. From what you
>> describe you are interested in matrices r(b) and e(b)
>>
>> sysuse auto, clear
>> generate lp=log(price)
>> regress lp weight length headroom mpg
>> mfx
>> matrix list e(b)
>>
>> Best, Sergiy
>>
>> On Sat, Jun 8, 2013 at 4:50 PM, Vũ Võ <[email protected]> wrote:
>>> Hi everyone,
>>>
>>> I am writing a code in do file to calculate the marginal effect of x
>>> on y (log-linear model)
>>>
>>> The function form as  following:
>>>
>>> Ln(Y) = a0 + a1*X + a2*X^2  + e ----> Y = exp(a0 + a1*X + a2*X^2)
>>>
>>> So, dY/dX = (a1 + 2*a2*X)*exp(a0 + a1*X + a2*X^2)        eq(1)
>>>
>>> If I run the regression by command line as:
>>>
>>> reg lnY x c.x#c.x
>>>
>>> after that using margins command:
>>>
>>> margins, dydx(x).
>>>
>>> I will obtain the marginal effect of x on Ln(Y):  (a1 + 2*a2*X).
>>>
>>> How can I calculate the dY/dX as in eq(1) by using the do file, not
>>> calculating manually?
>>>
>>>
>>> 2. Second question is:
>>>
>>> If I use command margins, predict(p)  and obtain the results as following:
>>>
>>> Marginal effects after hetprob
>>>      y  = Pr(y) (predict, p)
>>>         =  .54284206
>>> ------------------------------------------------------------------------------
>>> variable |      dy/dx    Std. Err.     z    p>|z|  [    95% C.I.   ]      X
>>> ---------+--------------------------------------------------------------------
>>>       x |   .3704576      .03237   11.44   0.000   .307015    .4339   .020114
>>>    xhet |  -.0736092      .02423   -3.04   0.002  -.121095 -.026124   .502716
>>>    off1 |  (offset1)                                                  1.00516
>>>    off2 |  (offset2)                                                  1.09709
>>> ------------------------------------------------------------------------------
>>>
>>> The question is how to store the marginal effects value, say in this
>>> case is 0.3704576,
>>>
>>> in the scalar in stata?
>>>
>>>
>>> Thank you so much.
>>>
>>> Vo Duc Hoang Vu
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>>
>> *
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>
>
>
> --
>
> Vo Duc Hoang Vu
> Faculty of Development Economics
> University of Economics HCMC
> 1 A Hoang Dieu Street
> Phu Nhuan Dist. Ho Chi Minh
> Vietnam
> Tel: +84 94 550 22 77 (VN); +31 624 63 29 45 (NL)
> Fax: +84 8 38477948 (VN)
> E-mail:[email protected]
> http://www.voduchoangvu.net
>
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