"Little" is not the adjective that springs to mind
for that help file.
More important, I don't think that help file answers
much of the question here.
As 0 and 1 are attainable, logit in the strict sense is
out of the question.
It seems to me that the main issue with a predictor that is
a proportion is what is the shape of the function relating
response | other predictors
to
proportional predictor | other predictors
and, setting aside the instrumental variable aspect here,
one handle on that might be given by added variable plots
after a plain multiple regression -- or graphical near
equivalents such as -mrunning- or -mlowess-. Use -findit-
to locate these user-written programs.
My first stab at this would be to consider some power of
the predictor, say root or square. That way 0 and 1 stay
as they are but you can bend the scale in the middle.
Nick
[email protected]
David Airey
Nick Cox has a little Stata help file on transformations.
ssc install transint
Marck Bulter
> I have a question that is not entirely related to Stata. Do hope
> that you forgive me.
>
> Assume the following model,
>
> *ivreg* pstrmon price maturity age coupon pstrmonprev pstrprev
> intrest ivol compl (precmon = precmonprev)
>
> Where pstrmon, pstrmonprev, precmon and precmonprev are all
> proportions. In this case, value bond A / total value bonds, etc.
> Therefore, it can take any value between 0 and 1, 0 and 1 included.
> These last 4 variables are heavily left skewed. Post estimations,
> resid is heteroskedastic, and resid is not normal distributed.
> On the Statalist server I have found several references to logistic
> transformations, ln(y/1-y):
> - http://www.stata.com/statalist/archive/2003-07/msg00285.html
> - home.fsw.vu.nl/m.buis/presentations/UKsug06.pdf
> - http://www.stata.com/statalist/archive/2006-02/msg00150.html
>
> If I transform the 4 variables using logistic transformation, the 4
> variables or no longer skewed, resid is almost homoskedastic, and
> resid is almost normal distributed.
> But my question is, is this transformation allowed, as I have mostly
> seen only references of transformation of the dependent variable.
> In addition, the transformation makes the interpretation of the
> coefficients hard, any comment on this?
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