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From | Stas Kolenikov <skolenik@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Bivariate probit model with partial observability and survey data |
Date | Sun, 21 Aug 2011 11:16:16 -0500 |
2011/8/21 Eduardo Andrés Alfonso Sierra <ealfonsosierra@googlemail.com>: > It starts Fitting comparison model and it finished after 339 iterations. > Then, in Fitting full model, it keeps on iterating, quite in the same > log likelihood value after 700 iterations and always showing backed > up. > Now, after more than 4 days running!, it keeps on going. > > I am aware of the poor convergence properties of this model, but I > really don´t get why the weights and adjustments for survey design > might change it so radically, so any idea on how to proceed would be > greatly appreciated. There are two issues with weights: 1. If there is a lot of variability (a coefficient of variation of say 10, and the ratio of the largest to the smallest weight of say a 1000), then the convergence (or the lack of it) and the estimates are essentially determined by the few largest weights. Look at -summarize weights- to see how bad the situation is. 2. -ml maximize- does not work well with weighted data if the scale of the weights is of the order say 1e4 or 1e5. I reported this earlier: http://www.stata.com/statalist/archive/2011-05/msg01412.html, and suggested the shortcuts to circumvent it. I should've recalled this discussion in the first place! I have been trusting -ml- to behave well so sincerely... but in my case, and apparently in your case as well, it does not. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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/