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<[email protected]> |

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<[email protected]> |

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

Date |
Fri, 11 Jan 2008 16:49:35 +0100 |

Does not that mean that this, > Turning of the effect of X: > 0.99%*1000000=9900 > > Turning on the effect of X: > 0.29%*1000000=2900 > > Then, the way I have understood this: > Discrete change, reduction induced by x=9900-2900=7000? Will be wrong? Here I have applied the on and off effects on the total sample. In the model n=1000000, and there is 500000 members of the program (x=1), thus 600000 are not member. I the example above I argue that x causes a reduction in the 1000000 sample of 7000, due to the dicrete change of .7%. Alex -----Opprinnelig melding----- Fra: [email protected] [mailto:[email protected]] P� vegne av Maarten buis Sendt: 11. januar 2008 16:11 Til: [email protected] Emne: Re: SV: st: From probit to dprobit to interpretation The separate probabilities need to be applied to their group, but the discrete change needs to be applied to the total sample. --- [email protected] wrote: > Thanks Maarten! That is very helpful. I guess what have been confusing > for me then is how to apply the predicted -0.7% discrete change (the > difference between turning on and off the effect), on the full sample > as I have done below, or only on those 500000 that are signed up to > the membership program. The difference offcourse making a huge impact > on the result. > > Best wishes, > Alexander > > -----Opprinnelig melding----- > Fra: [email protected] > [mailto:[email protected]] P� vegne av Maarten buis > Sendt: 11. januar 2008 14:16 > Til: stata list > Emne: RE: st: From probit to dprobit to interpretation > > What you say is correct and there is no contradiction between all > these statements. From a probit model you can derive predicted > proportions, and with predicted proportions you can derive predicted > counts in your sample (and if you know the size of your population the > predicted counts in your population). > > Hope this helps, > Maarten > > --- [email protected] wrote: > I have estimated a probit model where n=1000 000 customers with only > 1 independent dummy variable (x) (for the sake of clarity), and get > the following estimated coefficients: > > y_pred=-2.33-0.431*x (x being significant) > > No the way I understand this is that these coefficients, except for > the signs and significance level, is hard to interpret. Thus, I can > derive it as a probability model, and then again calculate > probabilities from any table with standard cumulative normal > distribution values. Turning on and off x will give me the discrete > change, thus > > 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% > > The difference between these probabilities is the discrete change, and > this change can be directly estimated using a dprobit model in Stata? > Discrete change=0.99-0.29=-0.7% > > Most textbooks stops here, and I think that so far I am on the right > track - but I want to interpret this probability in terms of what this > x induced effect means in terms of my sample... > > In this particular model my sample is 1000000, and x=1 is a membership > program of which there are 500000 members. Would it be correct to > assume that the discrete change estimated above in terms of customers > could be interpreted as following: > > Turning of the effect of X: > 0.99%*1000000=9900 > > Turning on the effect of X: > 0.29%*1000000=2900 > > Then, the way I have understood this: > Discrete change, reduction induced by x=9900-2900=7000? > > > > ----------------------------------------- > Maarten L. Buis > Department of Social Research Methodology Vrije Universiteit Amsterdam > Boelelaan 1081 > 1081 HV Amsterdam > The Netherlands > > visiting address: > Buitenveldertselaan 3 (Metropolitan), room Z434 > > +31 20 5986715 > > http://home.fsw.vu.nl/m.buis/ > ----------------------------------------- > > > ___________________________________________________________ > Support the World Aids Awareness campaign this month with Yahoo! For > Good http://uk.promotions.yahoo.com/forgood/ > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- __________________________________________________________ Sent from Yahoo! Mail - a smarter inbox http://uk.mail.yahoo.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: SV: SV: st: From probit to dprobit to interpretation***From:*Maarten buis <[email protected]>

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