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st: recursive cointegration
From
George Mawuli Akpandjar <[email protected]>
To
"[email protected]" <[email protected]>
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: [email protected]
[email protected]
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 <[email protected]>
To: "[email protected]" <[email protected]>
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: [email protected]
[email protected]
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 <[email protected]>
To: [email protected]
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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