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RE: st: Mediating variables


From   Jaime Gómez <jaime.gomez@unizar.es>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Mediating variables
Date   Sun, 5 Oct 2008 22:33:24 +0200

Thanks for your quick answer.
Yes, x predicts three mediators. They, in turn, predict y. Following Baron
and Kenny (1986) I want to see whether the introduction of one of the
mediators ("r") in the equation that expresses the relationship between "x"
and "y" changes the value or the significance of the coefficient
accompanying "x". This is the reason why the predictor ("x") is introduced
in all the equations of the system. 
Ideally, I would need to introduce the three mediators in my main equation,
but I am only focusing on one of them ("r"). In a paper by Shaver (2005) it
is suggested that 2SLS should be used to account for the correlation among
the errors. My doubts are:
1. I do not know whether omitting the other two mediators ("s" and "t") can
cause a problem and, in that case, I am looking for an econometric solution
2. I have data panel data: on different firms and for different years for
"x", "y" and "r"; I know that Stata gives you the possibility of estimating
an instrumental variables estimation with fixed effects, for example, but I
do not know whether this (or other alternatives) makes sense in this
context. In other words, even if 2SLS were the right procedure for
cross-section data, I do not know whether it would be the best alternative
in the presence of panel data
Thanks
Jaime Gómez
Universidad de Zaragoza

Baron, R.M. and Kenny, D.A. (1986) "The moderator-mediator variable
distinction in social psychological research: Conceptual, strategic, and
statistical considerations", Journal of Personality and Social Psychology,
51, 1173-1182

Shaver, J.M. (2005) "Testing for Mediating Variables in Management Research:
Concerns, Implications, and Alternative Strategies", Journal of Management,
31 (3), 330-353



-----Mensaje original-----
De: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de John Antonakis
Enviado el: domingo, 05 de octubre de 2008 21:06
Para: statalist@hsphsun2.harvard.edu
Asunto: Re: st: Mediating variables

If I understand your question correctly (from your first sentence), x 
predicts three mediators, which in term predict y.

This system is not identified for 2sls or 3sls analysis (you need at 
least as many IVs as you have mediators).

You could estimate it using mvreg or sureg (and then request whether 
errors are correlated like this):

sureg (y = r s t ) (r s t = x), corr

note: corr will give you a Breusch-Pagan test of independence (for the 
residuals)--a Hausman test will not help you here.

However, the above is not a strong test.

I am not following what you state regarding the panel structure.

HTH,
John.

____________________________________________________

Prof. John Antonakis
Associate Dean 
Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

http://www.hec.unil.ch/people/jantonakis&cl=en
____________________________________________________



Jaime Gómez wrote:
> Dear Stata users
> I have a model in which the relationship between a predictor “x” and an
> outcome “y” is mediated by three factors (“r”, “s” and “t”). I am only
able
> to test whether one of the predictors (“r”) mediates the relationship
> between “x” and “y” (I only have data on this mediating variable and I
> cannot get data on the other two). I would like to implement Baron and
Kenny
> (1986)’s test for mediation. At least, this involves estimating the
> following system:
> Y=a1+b*r+c*x+epsilon1
> r=a2+d*x+epsilon2
> Given that the errors of the two equations are potentially correlated, it
> has been suggested that a 2SLS approach should be used. I have seen  that
> this could be done with ivregress, provided that I can find data on at
least
> one variable that affects “r” and does not affect “y”. My doubts are the
> following:
> 1)	Given that I have a triangular system, do I have to use the
> traditional approach implemented by ivregress or the “modified” proposed
in
> http://www.stata.com/support/faqs/stat/ivr_faq.html ? Are both valid?
> 2)	How do I test for the hypothesis that the errors are correlated? I
> have seen that the use of a Hausman test is suggested in the literature,
but
> I do not know how to implement this in Stata (specially in the case I use
> the “modified” approach)
> 3)	Given that I have panel data, could I take advantage of the panel
> structure of my data to correct for the fact that I do not have
information
> on two of the mediating variables (“s” and “t”)? Is there a procedure in
> Stata for that?
> Thanks a lot 
> Jaime Gómez
> Universidad de Zaragoza
>
>
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