Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: gradient and the inverse of the information matrix |

Date |
Thu, 2 May 2013 11:15:58 +0200 |

--- On Wed, May 1, 2013 at 11:40 PM, Jun Xu wrote: >> I am working on a single-equation categorical dependent variable model. I estimated the model with constraints imposed. Then if I understand the score test correctly, it would be a simple matrix operation of >> >> gradient * inv(information matrix) * gradient' --- On Thu, May 2, 2013 at 9:44 AM, Maarten Buis wrote: > The gradient returned in e(gradient) is what it is supposed to be, but > not what you want it to be. What e(gradient) gives you is the gradient > of the constrained model at the estimated parameters of the > constrained model. What you want is the gradient of the > _unconstrained_ model at the parameter values of the constrained > model. The same is ofcourse true for e(V) -- Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * 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: gradient and the inverse of the information matrix***From:*Jon Mu <mystata@hotmail.com>

**Re: st: gradient and the inverse of the information matrix***From:*John Antonakis <John.Antonakis@unil.ch>

**RE: st: gradient and the inverse of the information matrix***From:*Jun Xu <mystata@hotmail.com>

**Re: st: gradient and the inverse of the information matrix***From:*Maarten Buis <maartenlbuis@gmail.com>

- Prev by Date:
**SV: st: Using values in an variable to save parts of an dataset** - Next by Date:
**st: esttab** - Previous by thread:
**Re: st: gradient and the inverse of the information matrix** - Next by thread:
**RE: st: gradient and the inverse of the information matrix** - Index(es):