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RE: st: Calculation of covariance matrix for unbalanced sample?


From   "Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Calculation of covariance matrix for unbalanced sample?
Date   Thu, 3 Nov 2011 07:51:32 -0500

Of course, there is no guarantee such a matrix will be positive (semi)definite. If you want that, you need to make further assumptions such as multivariate normal and then use maximum likelihood, multiple imputation or Bayesian methods.

Al

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Cameron McIntosh
Sent: Thursday, November 03, 2011 7:44 AM
To: STATA LIST
Subject: RE: st: Calculation of covariance matrix for unbalanced sample?

Nick, Stas
Just curious. What's the estimation method being applied below: EM, FIML, MI...?
Thanks,
Cam

> From: n.j.cox@durham.ac.uk
> To: statalist@hsphsun2.harvard.edu
> Date: Thu, 3 Nov 2011 12:30:22 +0000
> Subject: RE: st: Calculation of covariance matrix for unbalanced sample?
> 
> -makematrix- (SJ) can do this. But it's better to use Stas' custom code, which is more direct. 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> Stas Kolenikov
> 
> I don't think there's any. I vaguely remember a discussion some time
> back on the list about this. Here's the basic outline from scratch:
> 
> program define pwcovmat, rclasssyntax varlistunab vars :
> `varlist'local p : word count `vars'tempname Covmatrix `Cov' =
> J(`p',`p',.)matrix rownames `Cov' = `vars'matrix colnames `Cov' =
> `vars'forvalues i=1/`p' {  forvalues j=`i'/`p' {    local x : word `i'
> of `vars'    local y : word `j' of `vars'    quietly corr `x' `y', cov
>    matrix `Cov'[`i',`j'] = r(C)    matrix `Cov'[`j',`i'] = r(C)
> }}return matrix Cov = `Cov'end // of pwcovmat
> sysuse auto
> corr weight price mpg, cov
> corr weight price mpg rep, cov
> pwcovmat weight price mpg rep
> matrix list r(Cov)
> 
> On Thu, Nov 3, 2011 at 6:00 AM,  <S.Jenkins@lse.ac.uk> wrote:
> 
> > A colleague has data on a relatively large number of variables. His
> > sample is unbalanced in the sense that each variable has some missing
> > values. He wishes to calculate the covariance matrix for his data but
> > without the listwise deletion of cases that is imposed by -correlation,
> > covariance-  or -matrix accum-.
> >
> > My first thought was that he could use -pwcorr- and loop over his
> > variables, and build up his matrix from the saved results. But I thought
> > there must be an easier or more straightforward way -- but Googling and
> > -findit- have not suggested any.  I guess there is a relatively easy
> > Mata solution, but I am currently unfamiliar with that route.
> >
> > Suggestions using Stata or Mata please
> 
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