Replying to myself...
I suspect I've identified something I was previously doing wrong,
although problems continue. In the regression:
--------------
xi: clogit choiceflag NotOO Price OtherThings i.Group*NotOO
i.Period*NotOO i.Subject*NotOO, group(id)
--------------
...I was correctly treating NotOO, Price, OtherThings as features of
the alternative chosen, but the control variables are features of the
overall decision.
So, I'm trying to shift things to - asclogit - as follows:
--------------
xi: asclogit y NotOO Price OtherThings, case(id) alternatives(n)
basealternative(0) casevars(i.Group i.Period i.Subject)
--------------
and that leads to more-immediately-evident problems than the previous
approach, even if the previous approach was wrong. The output goes:
--------------
note: _Iuniquesub_48 dropped because of collinearity
note: _Iuniquesub_40 dropped because of collinearity
note: _Iuniquesub_32 dropped because of collinearity
note: _Iuniquesub_24 dropped because of collinearity
note: _Iuniquesub_16 dropped because of collinearity
note: variable r_minprice has 72 cases that are not
alternative-specific: there is no within-case variability
note: model has collinear variables; convergence may not be achieved
Iteration 0: log likelihood = -361.60637 (not concave)
--------------
(uniquesub is the actual variable name I've noted as "Subject" above,
and r_minprice is one of the "OtherThings".)
...and so on, (not concave) at every step, and failing to converge.
using the - difficult - option doesn't seem to help.
Question: does the warning "note: model has collinear variables;
convergence may not be achieved" mean that some variables are
perfectly collinear? I assume since it's dropping some collinear
variables that the warning means there are variables which are closely
but not exactly collinear?
In any case, I suspect that a problem like this is a matter of me
thinking more about my data, but if anyone has a clever idea, I'd love
to hear it.
Thanks
-Timothy
On 9/18/07, Timothy Dang <[email protected]> wrote:
> 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+b1*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.
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
------------------------------
Timothy O'Neill Dang / Cretog8
623-587-0532
One monkey don't stop no show.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
