Hello-
I'm estimating the rules for buyer purchase decisions in an experiment
with what amounts to panel data. Buyers can purchase any one of 3
goods or purchase nothing, and this occurs repeatedly for 20 periods.
The basic decision model is:
Pr(choice i) = e(b0+b1*Price_i+b2*OtherThings_i)
-----------------------------------------------------------------------------
1+ Sum(for j=1,2,3)[e(b0+b0*Price_j+b2*OtherThings_j)]
I'm taking each buyer decision and breaking it up into 4 records--one
for each possible decision, in order to use clogit for the estimation.
To control for dependencies I'm actually running the regression as:
xi: clogit choiceflag NotOO Price OtherThings i.Group*NotOO
i.Period*NotOO i.Subject*NotOO, group(id)
In the above "id" is a unique number for the original record before I
breaks it up into 4 records. So "id" is the number of a specific
decision. "NotOO" is a flag which is 1 when the decision is actually
to purchase something and 0 when the decision is to purchase
nothing--its coefficient will be the b0 in the model. Group, Period,
and Subject are all things which could be reasonably expected to have
interdependencies. I interact them with NotOO to maintain the
distinction between purchasing something and nothing.
The regression results seem reasonable. Individually, none of the
controls are significant, but jointly they are.
But I also want marginal effects, and when I ask for those the
marginal effects of the variables I'm actually concerned with are very
nearly zero and insignificant. I'm pretty sure this is wrong, and an
artefact of the way I'm doing the regression. When I leave off the
dummy terms the coefficients are very nearly the same, and marginal
effects are significant in both size and p-value. Actually, the one
exception to this is the constant term which goes from around 2 to
around 25, andfrom statistically significant to not, when the control
dummies are added.
So, I'm hoping for advice on either the mechanics or theory of what's going on.
As an aside, I'm using clogit rather than mlogit because of what
appears to be a bug with mlogit. When I run the same model with mlogit
(using a single record per decision instead of 4 records, and
constraints to fit the model), one of the coefficients for one of the
choices winds up getting dropped for no sensible reason.
Thanks
-Timothy
------------------------------
Timothy O'Neill Dang / Cretog8
623-587-0532
One monkey don't stop no show.
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