st: SUEST vs IV

[John Antonakis <John.Antonakis@unil.ch>]

Hi:

I am examining the effect of an endocrinological variable (X) as a 
predictor in a regression model; X correlates very strongly with being 
male.
Suppose the basic model is:

y = a0 + a1X + control variables + u

One way to account for the difference in X for males and females is to 
include the dummy variable male in the regression:
y = b0 + b1X + b2Male + control variables + e

I could also interact Male with X and all the predictor variables to 
remove any heterogeneity in the model due to being Male. Or a simpler 
way to do this is with seemingly unrelated estimation (suest). That is, 
I estimate the model separately for women and men and then stack the 
models with suest. In this way, I can clearly see the effect of X on y 
in both groups and do cross-equation tests as needed.
However, to see the general effect of X on y, while accounting for what 
causes X, yet another specification might be to instrument X with Male 
(because X is actually endogenous to Male):
ivreg2 y (X= male + other instruments) controls

If the Hansen J test is non-significant, it means that the effect of 
being Male on Y is mediated fully via X.
Of course, the suest and ivreg2 models are looking at different things; 
however, which is more defensible do you think?
I would be interested in your thoughts (and alternative modeling 
procedures if relevant).
Best,
J.

--
__________________________________________

Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
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

Associate Editor
The Leadership Quarterly
__________________________________________

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/