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Re: st: mlogit coefs


From   Chiara Mussida <cmussida@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mlogit coefs
Date   Fri, 20 Apr 2012 15:00:51 +0200

Thanks for all suggestions. I checked my dataset containing all the
employed covariates:

mlogit transition male_unmarried female_married female_unmarried age
agesq ncomp child northw northe centre Ubenef edu1 edu2 health
qu1nolav qu3nolav qu2nolav nopersincnolav noothersineq qu1ot qu2ot
qu3ot if age>=15 & age<=64, b(3)


the dummy indicators for quantile of non labour income "qu1nolav
qu3nolav qu2nolav" takes the value one only for few individuals n each
subsample of analysis. e.g. 9 indivv in qu1nolav for the first outcome
(transition==1), etc. I'm going to try to substitute the indicators
for the quantiles with a dummy variable for the presence absence of
non labour income.
My question is more general: Is it possible that specific dummy
variables that take the value 1 for few few indviduals do gen not
robust results? I mean The above mlogit results differ to the ones
obtained on only a subsample of the initial sample (e.g. 3 transitions
instead of 9).
I tried to re-estimate the model without the specific dummies quoted,
and the results semm to be robust to the two alternative model
specifications mentioned.

Thanks
Chiara



On 17/04/2012, David Hoaglin <dchoaglin@gmail.com> wrote:
> Dear Chiara,
>
> I have a comment, much more minor than Maarten's, but still useful.
>
> If the contribution of age is nonlinear, it may not be satisfactory to
> assume that the nonlinearity is quadratic (in practice it often is
> not).  You did not mention the number of observations; but since you
> have the entire labor force, you may have enough data to approach the
> functional form of age empirically.  One strategy would separate the
> values of age into disjoint intervals (as narrow as the data will
> support), include in the model a dummy variable for each interval
> except one, and plot the fitted coefficients of those dummy variables
> against the midpoints of the intervals.  If that plot looks quadratic,
> fine.  But it may suggest that a linear spline would be a better
> summary of the contribution of age (taking into account the other
> variables in the model).
>
> David Hoaglin
>
> On Tue, Apr 17, 2012 at 10:00 AM, Chiara Mussida <cmussida@gmail.com> wrote:
>> Dear All,
>> I run a mlogit model for 9 labour market outcomes (transitions between
>> the three states of employment unemployment and inactivity, therefore
>> 6 transitions and 3 permanences), like:
>>
>> mlogit transition male_unmarried female_married female_unmarried age
>> agesq ncomp child northw northe centre Ubenef edu1 edu2 health
>> qu1nolav qu3nolav qu2nolav nopersincnolav noothersineq qu1ot qu2ot
>> qu3ot if age>=15 & age<=64, b(3)
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>


-- 
Chiara Mussida
PhD candidate
Doctoral school of Economic Policy
Catholic University, Piacenza (Italy)
*
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