[R] Interpreting coefficient in selection and outcome Heckman models in sampleSelection

[Ott-Siim Toomet]

Hi Mark,

why do you need that?  If your task is to estimate how much your y changes
if x change, why not use simple OLS? (Well, right, you should be able to
use sampleSelection as well).

It shouldn't probably be hard to compute it -- it is just OLS marginal
effect + som kind of derivative of Inverse Mills Ratio.  A little more
tricky question is, what to do with dummies and factor variables.

As Arne told, we are open to incorporate your changes!

Best,
Ott

> Hi Mark!
>
> On Sun, Jan 3, 2010 at 9:08 PM, Mark Bulling
> <mark.bulling at googlemail.com> wrote:
>> Hi there
>>
>> Within sampleSelection, I'm trying to calculate the marginal effects for
>> variables that are present in both the selection and outcome models.
>>
>> For example, age might have a positive effect on probability of
>> selection,
>> but then a negative effect on the outcome variable. i.e.
>> Model<-selection(participation~age, frequency~age, ...)
>>
>> Documentation elsewhere describes one method for doing this in Stata
>> based
>> on Sigelman and Zeng: http://polisci.osu.edu/prl/Selection%20Models.pdf
>> -
>> see page 16.
>>
>> I'd like to replicate this in r, but wanted to check I'm not reinventing
>> the
>> wheel, before doing so.
>
> I don't know a function/method that does this in R. So if you want to
> implement this in R, I suggest that you add a "marginalEffects" (or
> similar) method for objects of class "selection" to the
> "sampleSelection" package. You can get (write) access to the source
> code of this package on R-Forge [1]. Please let me (and Ott) know if
> you need any assistance.
>
> [1] http://r-forge.r-project.org/projects/sampleselection/
>
> /Arne
>
> --
> Arne Henningsen
> http://www.arne-henningsen.name
>