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From |
John Antonakis <John.Antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Mediating variables |

Date |
Sun, 05 Oct 2008 21:06:08 +0200 |

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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**Follow-Ups**:**RE: st: Mediating variables***From:*Jaime Gómez <jaime.gomez@unizar.es>

**References**:**st: Mediating variables***From:*Jaime Gómez <jaime.gomez@unizar.es>

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