If I were your boss, I would be alarmed about removal of outliers on
what appear to be ad hoc grounds. Even in your context, it is not clear
that ignoring awkward facets of your data will necessarily lead to
better predictions.
Nick
[email protected]
Bas de Goei
Ow, I understand both points made, and I agree that a country could
perfectly have demand with a still standing standing economy. The
economic ground is that jewellery is supposedly a luxury good, making
people spend as much on it as they are able given other constraints,
i.e. food etc. If you have less GDP growth, you should therefore also
have less jewellery demand growth. Well, the point you made Martin,
shows a very interesting aspect of India, which is its incredibly
strong class society. The top class, which apparently makes up almost
all jewellery demand appears unaffected by the state of the economy.
Not in any other analysed country was this the case. Because of
simplification, and simply because my boss would not understand / want
this sophistication (also because of time constraints), I decided that
having a 0 intercept would be better justifiable and this was backed
up by the fact that the intercept had become insignificant after
removing some outliers. Also the resulting forecast fitted better with
my boss' expectations - as you might have guessed I am not exactly in
an academic environment at the moment. So I'm sorry if some things
appear illogical, but I have to work under some constraints. Well, the
only thing I'd really wanted was to calculate an R-squared for this
thing, so I'd be done with it. I think I have found my solution in
Kvalseths method, and I made it work (as far as I understand) with a
GLS regression as well by adjusting the OLS regression with the rho
from the stata output (though I had to calculate it manually in
excel).
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