I gather you have subsumed the additional regressors into the
"constant" in your example. With additional regressors, I think you
should predict at the values of the additional regressors observed in
the data, rather than at the mean. The value -3511 is one measure of
the ATT (putative effect of X on those with X=1) and twice that is one
measure of the ATE (the effect X would have on the whole sample if X=1
for everyone) in your example. Neither is a particularly defensible
estimate in the presence of other regressors of note, IMHO, especially
with observational (non-experimental) data. Others on Statalist and
within Statacorp have different opinions. Are you trying to merely
replicate output from -dprobit- or do you have a specific
counterfactual hypothesis in mind? The specific hypothesis should
motivate the method.
On Jan 11, 2008 3:08 PM, <[email protected]> wrote:
> Thank you, Austin. I am aware that tab y x will produce the same result. I was just trying to work out the logic going fra a probit to a dprobit reporting the dicrete change of a dummy. Then I wanted to find out how to apply these probabilities to do predicted counts. I have a sample of 1000000, where there are 500000 incidences where x=1. What I am curious about is whether the discrete change og 0.7 percentage point reduction could be apllied directly to the entire sample, or if it should be applied the 500000 incidences where x=1. That is, are this correct?
>
> >di round(5e5*normal(-2.33))
> >4952
> >di round(5e5*normal(-2.33-0.431))
> >1441
> >di round(5e5*(normal(-2.33-0.431)-normal(-2.33)))
> >-3511
>
> Or is this correct?
> di round(1e6*normal(-2.33))
> 9903
> di round(1e6*normal(-2.33-0.431))
> 2881
> di round(1e6*(normal(-2.33-0.431)-normal(-2.33)))
> -7022
>
> The way I understand this is that the discrete change should be applied to the entire sample. I have many other regressors, but these I set to equal their mean in a dprobit model. Though, the same logic should apply to the interpretation of the discrete change in the dummy variables.
>
>
> Best wishes,
> Alexander
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