Hi to All!!
I would like to know what should I do to be able to
interpret the dummy
variables with respect to the grand mean and not with
respect to the omitted
reference category.
One possibility recommended in the literature was to
use restricted least
squres method (Greene, Seaks 1991). It recommends to
set the restrictions to
dummies so that the weighted (according to weights of
observation in
endogenous variable) sum of all dummies (no reference
category is omitted) is equal to zero.
I couldn't use the constraint in the stata, as it
didn't recognise the sign
"*" in the constraint, e.g.
weight*dum_110+weight*dum_123....+weight*930=0.
There should also be some way of recalculating the
regression results of the
dummy variables got with the n-1 dummmies to
recalculate it to the
n-dummies. How can I do that? Or what would be easier?
It's problematic, that I have 3 different types of
dummies: for regions
dum_agr, ...dum_vit (106 regions); for industries
dum_011...dum_930 (200
industries) and for time dum_2001, dum_1991 (together
3 years).
I added here one stata output that is estimated with
omitted categories (n-1
dummies) for region, industry and year dummies.
I also added one pdf-article where in the page 399 the
restrictions are
explained, but as I said I couldn't insert the
constraint to the stata.
I would like to interpret my dummies followingly: if
the region dummy
coefficient is positive and significant, the
industries located in this
region have higher than average positive location
effect of this region.
If the year dummy is positive and significant, in this
year had the higher
employment growth supportive effect than the years
together in average.
How could I calculate coefficients for n dummies in
stata to avoid perfect multicollinearity trap??
The endogenous varibale is the employment growth in
industry i located in
the region r.
Best Regards
Javier
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