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Re: st: Explaining apparent inconsistency in regression coefficients
From
Charles Koss <[email protected]>
To
[email protected]
Subject
Re: st: Explaining apparent inconsistency in regression coefficients
Date
Fri, 8 Apr 2011 14:47:11 -0500
Since y1 and y2 can be zero and y3 can't since is the sum of y1 and
y2, then, 0.21 seems a resonable estimate given 0.45 and 0.35 (y1 ^
y2) as the slopes for variables that allow for zero values, they tend
to be higher in magnitue, i.e. they are steeper. In contrast, since y3
can not be zero, the coeficient is lower, causing lower change, a
flater curve in 2D.
Cheers,
Charles
--
Charles Koss
http://charlesonnet.blogspot.com
On Fri, Apr 8, 2011 at 2:23 PM, Hussain Samad <[email protected]> wrote:
> Dear Statalist:
>
> This is not exactly a STATA question except for the program is run in STATA. I
> am running a say OLS (not TOBIT) for three outputs:
>
> log(1+Y1)= alpha + beta*X + epsilon
> log(1+Y2)= alpha + beta*X + epsilon
> log(1+Y3)= alpha + beta*X + epsilon
>
> where Y1= farm income, Y2=nonfarm income, and Y3=total income=Y1+Y2, and X is a
> dummy intervention variable. Y1 or Y2 can be 0 but Y3 cannot. And these are the
> beta values I'm getting: 0.45 for Y1, 0.35 for Y2 and 0.21 for Y3. My
> question: why is the beta for Y3 is smaller than the other two? Shouldn't it be
> in between the other two betas? Any explanation will be highly appreciated.
>
> Thanks. Hussain
>
>
>
>
>
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