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Interpreting coefficient in selection and outcome Heckman models in sampleSelection
4 messages · Mark Bulling, Arne Henningsen, Ott-Siim Toomet
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
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
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