Dear Clive et al,
Thanks! Yes, I certainly understand that. However, it is not my
question. I am actually interested in the situation in which the
effect is significant when we run seperate estimations, whereas the
interaction term in the signle equation is not significant. Perhaps,
an example will clarify this.
Suppose: Y= a0+ a1*x + u
Let Z is binary variable. Now, we can run two sperate OLS for Z=1 and
Z=0.Suppose that a1 is significant for the OLS for Z=1
Alternatively, we have a single equation with interaction term:
Y= m0 + m1*x + m2*z*x + v
My question is how we can explain the case in which m2 is
insignificant? Can we still say x has signficant effect on Y only
given Z=1 as implied by the seperate OLS? If yes then is it better to
run seperate OLS than single OLS w/interaction term given that the
former can identify which sample of population impose a significant
effect of x on y?
Many thanks!
Quang
On 9/27/07, Clive Nicholas <[email protected]> wrote:
> Quang Nguyen wrote:
>
> > Thanks so much for your excellent answer, I highly appreciate it. I
> > have a follow-up question. Suppose, we run seperate OLS and have one
> > significant coefficience; however the interation term in the signle
> > equation is not significant. Waht can we say about the result.
>
> You shouldn't even have to ask this question! If you have been using
> interaction terms in your models, then you already ought to know what
> they are meant to tell you.
>
> Say you have an OLS model of the response (Y) as a function of, say,
> three covariates (X1, X2 and X3), where X3 is the interaction of X1
> and X2. If X3 is significant, then the effect of X1 on Y varies by
> values of X2. If it isn't, it doesn't.
>
> That's all there is to it.
>
> --
> Clive Nicholas
>
> [Please DO NOT mail me personally here, but at
> <[email protected]>. Thanks!]
>
> "Courage is going from failure to failure without losing enthusiasm."
> -- Winston Churchill
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