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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: mata optimize with d2debug |

Date |
Fri, 25 Sep 2009 09:50:32 -0400 |

Stas-- Do you know where the ridge is? Can you reparametrize to exploit prior knowledge of that nature and minimize the use of numerical techniques? I would also trust your analytical derivatives more as long as the steps are small; what is the parameter space like? On Thu, Sep 24, 2009 at 5:17 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > I am programming a fairly large model and rather poorly identified > model with a couple dozen parameters in Mata. Documentation on > -mf_optimize- says: "When you have done things right, gradient vectors > will differ by approximately 1e–12 or less and Hessians will differ by > 1e–7 or less." I never get there; even a restricted version of the > model that is known to converge well produces mreldifs of about 1e-7 > and 1e-4, respectively. The mreldifs for the Hessian might start kinda > high between 1 and 10, but they would eventually go down near the > maximum. For the interesting (and poorly identified) models that I > eventually want to fit, I get mreldifs around 1e-3 to 1e-5 in > gradients for most iterations, while my mreldifs for the Hessian > fluctuate between 1e-3 and 1. In the early steps far from the maximum, > the mreldifs for the Hessian can be as large as 100 or so (that's for > 20x20 matrix, remember), but they go down as I converge to the > maximum. Since I am walking along a ridge, I would actually tend to > trust my analytical derivatives more than I do the numeric > derivatives. Is that reasonable? Any advice on this? I tried tighter > convergence criteria, but the results did not change much. * * 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/

**References**:**st: mata optimize with d2debug***From:*Stas Kolenikov <skolenik@gmail.com>

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