RE: st: recursive cointegration

[Cameron McIntosh <cnm100@hotmail.com>]

George,

 When I see abrupt questions like this with no context (on any listserv), I often wonder (nervously) how much background reading the poster has done on the statistical method being applied, in this case, cointegration. I could be wrong -- perhaps you fully the rationale for the procedure and just want the specific Stata syntax, which I imagine you could have found yourself with a bit of searching. Anyway, it's not just a one-liner that someone can copy into an email, especially without knowing anything about your model. I think you're going to have to do the programming yourself. To that end, have a look at:

Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector 
Auto-Regressive Models. Oxford, UK: Oxford University Press.


Prazmowski, P. (2005). A recursive cointegration test using the Kalman filter and its application to fiscal equilibrium in the Dominican Republic.
Applied Economics Letters, 12(3), 155-160.

Joly, P., Heinecke, K., & Morris, C. (June 24, 2001). JOHANS: Stata module to perform Johansen-Juselius ML estimates of cointegration. 
http://ideas.repec.org/c/boc/bocode/s419401.html

Stata 12 Help:

http://www.stata.com/help.cgi?vec

My two cents,

Cam

> Date: Fri, 7 Oct 2011 12:54:58 -0700
> From: agmk18@yahoo.com
> Subject: st: recursive cointegration
> To: statalist@hsphsun2.harvard.edu
> 
> 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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