Re: st: biprobit postestimation (marginal effects)

[Fabian Guy <fabolous.vs@gmail.com>]

Thanks for the reply and sorry for being unprecise at the points a. to c.

Basically I'd like to compute the following two marginal effects:
1.  dP(y1==1|X,L.y1,y2,L.y2)/dL.y2 and dP(y1==1|X,L.y1,y2,L.y2)/dX_j
(marginal effect of the binary lagged variable & marginal effect of a
regressor j)
2. dP(y1==1|X,L.y1,y2,L.y2)/dy2

Do you know any reference or minimal example where I can look up how
to calculate these kind of marginal effects using the command predict?

Thank you very much.

2013/9/19 Fabian Guy <fabolous.vs@gmail.com>:
> Thanks for the reply and sorry for being unprecise at the points a. to c.
>
> Basically I'd like to compute the following two marginal effects:
> 1.  dP(y1==1|X,L.y1,y2,L.y2)/dL.y2 and dP(y1==1|X,L.y1,y2,L.y2)/dX_j
> (marginal effect of the binary lagged variable & marginal effect of a
> regressor j)
> 2. dP(y1==1|X,L.y1,y2,L.y2)/dy2
>
> Do you know any reference or minimal example where I can look up how to
> calculate these kind of marginal effects using the command predict?
>
> Thank you very much.
>
>
>
> 2013/9/19 Austin Nichols <austinnichols@gmail.com>
>>
>> Fabian Guy <fabolous.vs@gmail.com>:
>>
>> You have to write a program to predict the relevant probabilities and
>> calculate differences, then you can bootstrap the program to get SE
>> for your marginal effects. You should not assume "X is at the mean of
>> the sample" rather than simply predicting the relevant probability for
>> each sample case, then averaging across the sample, to get the mean
>> marginal effects rather than marginal effects at the mean; you would
>> not want a marginal effect for a sample case that is half female and
>> half male rather than the half the effect for males plus half the
>> effect for females.
>>
>> On a more practical note, it is convenient to rename the variable for
>> which you want to impose a counterfactual value, then generate a new
>> variable with that name e.g. generate y2=0, predict, then drop the new
>> variable and rename back to your original data.
>>
>> I can't tell how many marginal effects you really want to estimate in
>> your a,b,c below, given the 0 (1) notations, but perhaps you can
>> explain what the primary marginal effect of interest is.  For
>> instance, in item a, I would guess you want to know
>> dPr(y1==1|X,L.y1,y2,L.y2)/dL.y2 which means you average over the y2
>> and L.y1 and L.y2 possibilities, perhaps using observed lag values but
>> probabilities of y2 in your sample.
>>
>> On Thu, Sep 19, 2013 at 6:10 AM, Fabian Guy <fabolous.vs@gmail.com> wrote:
>> > Dear Stata-Experts,
>> > I need your advice with a simple biprobit postestimation analysis. I
>> > think the problem that I have could be solved in a straightforward
>> > manner, but since I am not that familiar with Stata I would like to
>> > make sure that I do not mess things up.
>> >
>> > So, suppose I have a panel of i=1,...,I individuals and for each
>> > individual I have observation over time t=1,...,T.
>> > I estimate the following _pooled_ bivariate probit model:
>> > (y1 = L.y2 X)
>> > (y2 = L.y1 X)
>> > where X = common regressors in both equations (continuous, no binary
>> > variable included in this set). The estimation turns out that
>> > estimating a two equation probit is important, since the correlation
>> > parameter of the errors is significantly different from zero.
>> >
>> > I would like to compute the following marginal effects:
>> > a) Given X is at the mean of the sample, y2=0 (1), and L.y1=0 (1),
>> > what is the increase/decrease in the probability of y1=1 if L.y2
>> > increases from 0 to 1?
>> > b) Given X_subset is at the mean, y2=0 (1), and L.y1=0 (1), what is
>> > the increase/decrease in the probability of y1=1 if X_j increases by
>> > one unit?
>> > c) Given X is at the mean, L.y2 = 0 (1) y2=1 (0), and L.y1 = 0 (1),
>> > what is the Prob. of y1=1?
>> >
>> > For me it looks like that those marginal effects could be typically
>> > requested using biprobit. Do I have to use margins or predict for
>> > these calculations? Do I have to code this by my own or is there a
>> > Stata command with some options (like predict/margins) that could
>> > provide me a solution to those calculations?
>> >
>> > A possible variation would be to set L. variables as well to their
>> > average, however, I think it does not make much sense for dichotomous
>> > variables.
>> >
>> > I appreciate any advice very much.
>> >
>> > Best,
>> > Fabian
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>
>
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