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From |
Nick Cox <njcoxstata@gmail.com> |

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

Subject |
Re: st: non-linear models not converging |

Date |
Wed, 22 May 2013 14:11:32 +0100 |

-nl- does not use maximum likelihood. -xtpoisson- does. As you did not specify -xtpoisson- in your first post, none of us could write comments geared to that command. You can reach through -xtpoisson- to specify initial values. See the help and click on maximize_options. But you have extra options, including changing -tech()-. For example, I find -tech(bhhh)- works wonders, and I have no precisely zero idea why. But I like it nevertheless. However, no trick is guaranteed to work. At a rough guess from Statalist postings, most models that don't converge in Stata are just a bad idea and there is no easy fix for that. Nick njcoxstata@gmail.com On 22 May 2013 13:59, James Bernard <jamesstatalist@gmail.com> wrote: > Thanks Nick, > > I am not writing the MLE. I am using -xtpoisson > > How can I supply the initial value for the numeric solution? > > Thanks, > James > > > On Wed, May 22, 2013 at 8:55 PM, Nick Cox <njcoxstata@gmail.com> wrote: >> I presume focus on -nl-. >> >> Convergence is more likely if >> >> 1. the model is actually right for the data in a qualitative sense >> (easy to say, hard to define, obvious when it fits well) >> >> 2. you supply good initial guesses for the parameters (this is perhaps >> the easiest one to tweak) >> >> 3. you are estimating a small number of parameters >> >> 4. you have a good ratio of data points to parameters >> >> 5. the data are not grotesquely behaved (e.g. outliers and high >> skewness can be just as problematic as with linear models) >> >> 6. the model is not highly nonlinear (the textbooks are full of this) >> >> 7. I like lists to have about 7 items, so something else belongs here. >> >> Maarten Buis should have a Euro for every time he's recommended >> retreating to a simpler model when a complicated one doesn't converge, >> and then adding complexity one step at a time. But it's good advice. >> >> Nick >> njcoxstata@gmail.com >> >> >> On 22 May 2013 13:43, James Bernard <jamesstatalist@gmail.com> wrote: >>> Hi all, >>> >>> I understand that the numeric methods used for estimation of models in >>> Stata (and any other package) may result in a model that does not >>> converge. >>> >>> Do you happen to know of any trick to help make the model converge? To >>> increase the chance of converging? >>> >>> Thanks, >>> James >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: non-linear models not converging***From:*James Bernard <jamesstatalist@gmail.com>

**Re: st: non-linear models not converging***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: non-linear models not converging***From:*James Bernard <jamesstatalist@gmail.com>

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