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Re: st: Mediating variables
OK....so you could do:
ivreg2 y t (r s = x q)
(if you don't have it type findit ivreg2. It is an extension of the official ivreg STATA command)
If you have panel data you can run:
xtivreg2 y t (r s = x q)
(for fixed-effects only).
Otherwise, xtivreg y t (r s = x q), re
will do the trick if you have random effects.
Problem with the above is if "t" is endogenous then the model is
mispecified.
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:
Thanks John
In fact, looking at my data I have information on one more variable, "q",
that could be used to estimate "r". This variable "q" is directly linked to
"r" in the literature (it is a predictor of "r"), but does not have a direct
relationship with "y". If I understand you correctly, this should allow me
to obtain an estimation for r that could be introduced in my main equation,
perhaps using "ivreg". In any case, I still do not know whether this solves
the problem of not having data on the other two mediators (intuitively, I
imagine that if "s" and "t" are not introduced as mediators, this could
create correlation between the error term of my main equation and the
predictor "x", given that "x" is also a predictor of "s" and "t"). From your
answer, I assume that the panel structure of the data is of no use in this
context. Is that ok?
Thank you very much.
Jaime Gómez
Universidad de Zaragoza
-----Mensaje original-----
De: [email protected]
[mailto:[email protected]] En nombre de John Antonakis
Enviado el: domingo, 05 de octubre de 2008 22:58
Para: [email protected]
Asunto: Re: st: Mediating variables
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:
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: [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
Universidad de Zaragoza
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