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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Prediction after a Xtlogit |

Date |
Wed, 16 Feb 2011 10:56:53 +0000 (GMT) |

--- On Tue, 15/2/11, Milet Emmanuel wrote: > How can marginal effects be calculated after a Xtlogit with > the predict(pc1) option? > Apparently it cannot - given the error message Stata > delivers. You have answered your own question. > The other option, predict(pu0) can nonetheless be used. The > only problem is that it assumes that the fixed effect is > null - something that bothers me quite a lot actually. > So, does anyone know a way to go around this difficulty ? Yes, interpret your results in terms of odds ratios. What makes the effect of a continuous variable in a linear model easy to interpet? An extra unit of x (the continuous variable) will always lead to b (the coeficient) increase in the predicted of y (the dependent variable). This is true regardless of how much y one had to begin with. This is an assumption that could be wrong, but it is the main strategy that makes linear regression models so useful. -(xt)logit- models use a similar strategy, here an extra unit of x leads to an increase or decrease of the expected odds by a factor exp(b), regardless of how high the odds was to begin with. So unlike marginal effects, you do not need to know the values of the fixed effects, they just do not matter for the odds ratio. For a similar argument (not surprising as I also wrote that one) see: <http://www.stata.com/statalist/archive/2011-01/msg00123.html> An alternative, would be to consider what you are doing when you compute marginal effect. You estimated a model that implies a non-linear effect on the probabilty and than you try to find some linear approximation of that effect. Basically, you are fitting a linear model on top of your non-linear model. If all you are going to do is interpet the results of this linear model (i.e. the marginal effects) then the obvious question would be: Why not cut out the middle man, and directly estimate a linear probablity model? You would need to have some faith in the robustness of these models to violations of model assumptions, but with marginal effects some awkward assumptions are also unavoidable. This is just to say that models aren't supposed to be true, they are just supposed to be useful. But if these approximations bother you then you can always fall back on the odds ratio. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Prediction after a Xtlogit***From:*Milet Emmanuel <emmanuel.milet@cepii.fr>

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