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st: recursive cointegration


From   George Mawuli Akpandjar <agmk18@yahoo.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: recursive cointegration
Date   Fri, 7 Oct 2011 14:34:51 -0700 (PDT)

Deal All,
What is the command for recursive cointegration test
Thanks

George Mawuli AKPANDJAR
(PhD Candidate)
Department of Economics
   University of Mississippi
      P.O.Box 4361
         University, MS 38677
Tel: +1-662-202-2434
Email: agmk18@yahoo.com
          gakpandj@olemiss.edu


 the lord is my shepherd i shall not want and i will dwell in his house forever
life isn't about waiting for the storm to pass...it's learning to dance in the rain.


----- Original Message -----
From: George  Mawuli Akpandjar <agmk18@yahoo.com>
To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Cc: 
Sent: Friday, October 7, 2011 2:54 PM
Subject: recursive cointegration

Deal All,
What is the command for recursive cointegration test
Thanks


George Mawuli AKPANDJAR
(PhD Candidate)
Department of Economics
   University of Mississippi
      P.O.Box 4361
         University, MS 38677
Tel: +1-662-202-2434
Email: agmk18@yahoo.com
          gakpandj@olemiss.edu


 the lord is my shepherd i shall not want and i will dwell in his house forever
life isn't about waiting for the storm to pass...it's learning to dance in the rain.


________________________________
From: Tiago V. Pereira <tiago.pereira@mbe.bio.br>
To: statalist@hsphsun2.harvard.edu
Sent: Friday, October 7, 2011 2:09 PM
Subject: RE: st: Spearman correlation with adjustment

Cecilia,

You might explore the following approach (and see if it makes some sense
in your case):

Assumption:  there are no ties. So, you can compute  spearman's
coefficient (rho_S) from  pearson's coefficient (rho_P)

Approach:

1) Create two or more categories or subgroups in which the confounding
variable has a smaller role
2) Within each category compute ranks for your values
2) Calculate the pearson coefficient using those ranks (that is, rho_P
will be calculated from ranked variables)
3) transform the rho_Ps into Z scores (r to z' transformation - Fisher
approach)
4) perform a meta-analysis of Z scores
5) get the results back to the original metric (rho_P)

This approach is likely to provide less biased results compared to raw
analyses. It also provides the opportunity to check/quantify if there is
statistical heterogeneity among subgroups (Cochran's Q test, I^2 index).

All you need is the rho_P from ranked variables and the following packages
-corrci- (or -corrcii-) and -metan-

also check:
http://mason.gmu.edu/~dwilsonb/ma.html
http://www.stata.com/statalist/archive/2010-06/msg00728.html

Cheers!

Tiago




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