[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

SV: SV: SV: st: From probit to dprobit to interpretation

From   <[email protected]>
To   <[email protected]>
Subject   SV: SV: SV: st: From probit to dprobit to interpretation
Date   Fri, 11 Jan 2008 21:08:08 +0100

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))
>di round(5e5*normal(-2.33-0.431))
>di round(5e5*(normal(-2.33-0.431)-normal(-2.33)))

Or is this correct?
di round(1e6*normal(-2.33))
di round(1e6*normal(-2.33-0.431))
di round(1e6*(normal(-2.33-0.431)-normal(-2.33)))

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,

-----Opprinnelig melding-----
Fra: [email protected] [mailto:[email protected]] P� vegne av Austin Nichols
Sendt: 11. januar 2008 19:29
Til: [email protected]
Emne: Re: SV: SV: st: From probit to dprobit to interpretation

I don't understand the numbers in your example:
 y_pred=-2.33-0.431*x (x being significant)  Turning off the effect of X thus gives me: y_pred=-2.33-(0.431*0) and  Pr(z<2.33)=0.99%  Tuning on the effect: y_pred=-2.33-(0.431*1)=-2.761 and  Pr(z<2.761)=0.29% "
Whether x is significant or not makes no difference.  If you want the marginal effect of a discrete change for a regression model showing  y_pred=-2.33-0.431*x would you not show  di normal(-2.33-0.431)-normal(-2.33) which is -.00702184 or a 0.7 percentage point reduction from about
4952 expected to have positive outcomes in a sample of 5e5 to about
1441 cases?

di round(5e5*normal(-2.33))
di round(5e5*normal(-2.33-0.431))
di round(5e5*(normal(-2.33-0.431)-normal(-2.33)))

But with a single regressor, you could get this from  tab y x more easily!

With additional regressors, there are more options for calculating marginal effects.

On Jan 11, 2008 11:57 AM,  <[email protected]> wrote:
> Thanks again for helping me with the logic of these calculations.
> It turned on to be a bit on the side of a typical Stata topic, sorry about that.
> Alex
*   For searches and help try:

*   For searches and help try:

© Copyright 1996–2024 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index