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

From   Maarten Buis <>
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
Reichpietschufer 50
10785 Berlin
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