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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
st: RE: Marginal Effects after Biprobit with Reliable Standard Errors? |

Date |
Tue, 1 Jun 2010 20:14:13 +0100 |

I have two banal suggestions. They may or may not apply to the specifics not given here. 1. Sometimes the model being fitted is just too complicated. Omitting some predictors may improve the situation with standard errors. 2. Sometimes this is Stata's way of telling you that the model is straining very hard to fit the data. Rethinking the functional form may be in order. In other words, fixing the poorly determined standard errors is a lesser issue than getting the right model in the first place. As I said, these are banal! Nick n.j.cox@durham.ac.uk Claudia Berg I am trying to obtain marginal effects with reliable standard errors after a Seemingly Unrelated Biprobit model. I have tried using the commands "mfx compute, predict()" but Stata warns that it is "unsuitable" for biprobit and imposes the option "nose". I know that the option "force" can be used to obtain standard errors but with no guarrantee that they are reliable. I have refered to the earlier discussion on statalist found at http://www.stata.com/support/faqs/stat/mfx_nose.html which says that: "if diag(vce) shows a large relative difference (say, bigger than 10^-2 for example) the standard errors given by using force will probably be wrong..." I checked the "diagnose(vce)" option for my data and found that for my data the relative difference was about 0.029. Can anyone suggest a way to get reliable standard errors? If I am forced to use "force", how unreliable would the standard errors be? * * 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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