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From |
Jn <ensam21@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: computing average partial effect in nonlinear models using forecasted distribution of x-variables |

Date |
Tue, 25 Mar 2008 08:22:59 -0400 |

Thanks for that helpful quote. I did notice if I were to run a standard linear regression before working with -adjust- command, my new predicted values were in the right range (around 0.10). But if I use -logit-, I get values around (0.05) which is way off and makes no sense. Given that I am trying to use the future projected mean for some of my x-variables (which is why I am doing running these postestimation commands in the first place), I don't think there is a way around fixing my problem if I were using a logit regression. Do you think it would be far too incorrect for me to run a standard linear regression just for this purpose only (forecasting future probability of a positive outcome)? At least I get reasonable predicted values that way.. - student On Tue, Mar 25, 2008 at 7:09 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > This is discussed in Buis (2007) "predict and adjust with logistic > regression", The Stata Journal, 7(2), pp. 221-226. > http://www.stata-journal.com/article.html?article=st0127 > > The reason for the difference is that -logit- implies a non-linear > transformation, so it makes a difference whether you first create > predicted values and than compute the mean, or when you first compute > the means of explanatory variables and than compute a predicted value. > To quote from the article: "It is the difference between a typical > predicted probability for someone within a group and the predicted > probability for someone with typical values on the explanatory > variables for someone within that group." > > Hope this helps, > Maarten > > > --- Jn <ensam21@gmail.com> wrote: > > I am trying to get at the magnitude of a change in Pr(y=1|x) by > > replacing each explanatory variable with its sample average, save for > > my variable of interest, which I was hoping to use a future projected > > distribution (I'm trying to see how this change in distribution of > > this certain binary independent variable changes the probability of > > y=1). I had no problem doing this with linear regressions (replace > > all > > variables with its sample mean, except use projected distribution for > > my variable of interest, do a linear prediction, note the > > difference). > > However, when I try to carry out the same procedure in a logit > > regression, I am running into problems. I was under the impression > > that, if I were to replace ALL of my independent variables with its > > sample mean and then run -predict-, I should get the same predicted y > > value as if I were to just run a normal regression without replacing > > my x-var with its sample mean. Am I wrong? I hope I am making myself > > clear.. > > > ----------------------------------------- > 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/ > ----------------------------------------- > > > > ___________________________________________________________ > Rise to the challenge for Sport Relief 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/

**Follow-Ups**:**Re: st: computing average partial effect in nonlinear models using forecasted distribution of x-variables***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: computing average partial effect in nonlinear models using forecasted distribution of x-variables***From:*Jn <ensam21@gmail.com>

**Re: st: computing average partial effect in nonlinear models using forecasted distribution of x-variables***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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