The manual recommends using iweights for this. See e.g. the
discussion under _robust, which gives an example of the robust
variance estimator calculated by hand.
--Mark
From: "Michael Blasnik" <[email protected]>
To: <[email protected]>
Subject: st: Re: Matrix Algebra
Date sent: Mon, 7 Oct 2002 07:42:34 -0400
Send reply to: [email protected]
> I should have looked in the manual before suggesting using aweight.
> The manual states that the weights are normalized when using aweights
> with mat accum. You can use pweights instead and get the answer you
> seek.
>
> Michael Blasnik
>
>
> ----- Original Message -----
> From: "Alfonso MIranda" <[email protected]>
> To: <[email protected]>
> Sent: Monday, October 07, 2002 5:00 AM
> Subject: st: Matrix Algebra
>
>
> > Note: Apologies if this mail comes out twice.
> >
> >
> > Dear Statalisters,
> >
> > I am trying to estimate a count model with endogenous switching as
> > proposed by Terza(1998). It involves the use of a two-step method of
> > moments estimator. First stage is done using a probit and second
> > stage is done using non-linear least squares. I basically have coded
> > all but the correction for the covariance matrix. For calculating
> > such a matrix I have to create an intermediate matrix that has the
> > following general form:
> >
> > Y = A'*W*A
> >
> > A is nxk matrix and W is a nxn diagonal matrix with regression's
> > squared errors in the diagonal and zeros elsewhere. Lets say that I
> > have variables a1 a2 a3 forming matrix A. And that the squared
> > errors are saved as variable res2. I have more than 20,000
> > observations in my dataset.
> >
> > Since it is not possible to create matrices of 20,000x20,000 in
> > Stata, I was very kindly suggested by Michael Blasnik to use the
> > weighting feature of matrix accum for calculating matrix Y.
> > Basically he suggested to use:
> >
> > .mat accum a1 a2 a3 [aw=res], noc
> >
> > In order to check that this solution is correct I drop observations
> > after estimating my model, and residuals, and kept only 200
> > observations. Then, as also kindly suggested by Nick Cox, I
> > calculate matrix A and W in the following way:
> >
> > .mkmat a1 a2 a3, matrix(A)
> > .local n = _N
> > .matrix W = J(`n',`n',0)
> > .forval i=1/`n' { W[`i',`i']=res2[`i'] }
> > .matrix mymat = A'*W*A
> > .matrix list mymat
> >
> > .symmetric mymat[3,3]
> > a1 a2 a3
> > a1 62054.362
> > a2 60504.697 60504.697
> > a3 1004.4707 1004.4707 1004.4707
> >
> >
> > This matrix is what I want but with large data it cannot be
> > calculated using Nick's suggestion. Now, using the weighting feature
> > of matrix accum
> >
> > .matrix accum H = a1 a2 a3 [aw=res2], noc
> > (sum of wgt is 2.4974e+03)
> > (obs=200)
> >
> > .matrix list H
> >
> > symmetric H[3,3]
> > a1 a2 a3
> > a1 4969.5487
> > a2 4845.4456 4845.4456
> > a3 80.441821 80.441821 80.441821
> >
> > Clearly, mymat and H are different. Jiang, Tao very kindly suggested
> > an alternative which would be:
> >
> > .sca m=10000
> > .matrix accum Z= a1 a2 a3 [fw=res2+m], noc
> > .matrix accum Z2= a1 a2 a3, noc
> > .matrix Z=Z1-m*Z2
> >
> > however, since res2 is not a integer number, frequency weights
> > cannot be estimated. I did what Jiang, Tao suggested using analytic
> > weigths:
> >
> > sca m=10000
> > matrix accum Z1= g1c g1cat g1ind [aw=res2+m], noc
> > (sum of wgt is 2.0002e+07)
> > (obs=200)
> >
> > matrix accum Z2= a1 a2 a3, noc
> > (obs=200)
> > matrix Z=Z1-m*Z2
> >
> > matrix list Z
> >
> > symmetric Z[3,3]
> > a1 a2 a3
> > a1 -4.670e+08
> > a2 -4.438e+08 -4.438e+08
> > a3 -4512856 -4512856 -4512856
> >
> > Which is also different to maymat. It seems then that all suggested
> > strategies do not yield the matrix that I need. Does anyone has
> > other idea?
> >
> > Many thanks,
> >
> > Alfonso Miranda
> > University of Warwick
>
>
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Prof. Mark E. Schaffer
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