# Re: st: Mediating variables

 From John Antonakis <[email protected]> To [email protected] Subject Re: st: Mediating variables Date Sun, 05 Oct 2008 22:57:36 +0200

OK...then just estimate

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

est store mediators

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

hausman mediators, equations(1:1)

If the Hausman test is undefined, do it by hand (Wooldridge has the formulas).

Note: you want tosee if x drops when introducing r s t.

I am just showing you the mechanics of how to run the tests. Again, I would feel better running a 2sls or reg3 model; but you need to have more instruments (not just x) to estimate this.

Best,
J.

____________________________________________________

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:

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

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: [email protected]
[mailto:[email protected]] En nombre de John Antonakis
Enviado el: domingo, 05 de octubre de 2008 21:06
Para: [email protected]
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

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