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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
RE: st: Marginal Effect |

Date |
Wed, 5 Jan 2011 22:26:43 +0000 (GMT) |

--- On Wed, 5/1/11, sazas wrote: > > > I estimated a random effect probit model using xtprobit in > > > stata 11. > > > > > Marginal effects using margins, dydx(*) predict(pu0) > > > assumes u_i =0 which l think implies rho = 0 > > > since rho = 0. Please is there any command that l can use > > > to change this assumption? --- I answered: > > It is not true that rho (proportion of variance due to > > group level variance) is assumed to be 0. The way to think about > > it is that there is an (unobserved) group level variable added > > to your model and that you compute your marginal effects for > > individuals that have an average value on this variable. --- On Wed, 5/1/11, [email protected] wrote: > I thought if the unobserved individual effect i. e u_i = 0 > implies that rho which is the relative importance of the > unobserved effect is 0. I think it is same as the > neglected heterogeneity in the estimation of partial > effect Wooldridge was refering to in his book on > chapter 15. If you want average marginal effects you will actually get a sort of mixture between average marginal effects over the observed variables and marginal effects at the average value of the unobserved group level variable. That is not very pretty, but it is not strictly speaking wrong (as long as you interpret it correctly). This "un-pretty-ness" is rather typical for the friction you can expect when using marginal effects for this type of models, as marginal effects will always give you somewhat "wrong" results: They are one-number-summaries of non-linear effects and such a summary is always wrong and this tends to bite more in more complicated models. However, as long as you interpret the marginal effects correctly, they can still be useful. Marginal effects are a model on top of a model, if used correctly they are wrong but still usefull summaries/ simplifications. As I said before, if you really want to have a more pure representation of the effect then you should forget about marginal effects, and move to -(xt)logit- models and interpret odds ratios. Odds ratios follow directly from these models, and do not need the "model on top of a model" approach of the marginal effects. However the added precision you obtain by doing this only involves the description of your model results, typically the amount of random noise that occured during data collection, data preparation, modeling decisions, are many orders of magnitude bigger than the precision you obtain by moving from marginal effects to odds ratios. Having said all that, my answer to your question is that you can get fully correct descriptions of your model results by using -xtlogit- and interpret your results in terms of odds ratios. 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/

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