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From |
"s_azagba@live.concordia.ca" <s_azagba@live.concordia.ca> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Marginal Effect |

Date |
Thu, 6 Jan 2011 16:16:01 +0000 |

Hi All, 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? Pls any other suggestion on what to do _______________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of s_azagba@live.concordia.ca [s_azagba@live.concordia.ca] Sent: Wednesday, January 05, 2011 3:36 PM To: statalist@hsphsun2.harvard.edu Subject: RE: st: Marginal Effect Hi Marten, 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. ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Maarten buis [maartenbuis@yahoo.co.uk] Sent: Wednesday, January 05, 2011 1:46 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Marginal Effect --- 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? 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. I do not think that is a too problematic assumption, but there is always a bit of friction when using marginal effects for this type of models. In essence you are trying to fit a linear line to a non-linear one, which will often (but not always) produce an ok summary of the non-linear line, but it will never be exactly right. If you are a purist, then you should probably use -xtlogit- and interpret the odds ratios rather than the marginal effects. In practice, I would use which ever model and effect size I prefer, and look at tables of predicted probabilities and odds, look at graphs of predicted probablities and odds and look if I can (approximately) match those with the effect sizes I found. If you can do that, then there isn't much of a problem. 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/ * * 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/ * * 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/

**Follow-Ups**:**RE: st: Marginal Effect***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: Marginal Effect***From:*sazas <s_azagba@live.concordia.ca>

**Re: st: Marginal Effect***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**RE: st: Marginal Effect***From:*"s_azagba@live.concordia.ca" <s_azagba@live.concordia.ca>

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