Importance weights do whatever you want them to do. For example,
specify the iweight to be more than 1, say 10, and in the auto dataset
regress will give you 7 observations only. Thus, it can do the
opposite of what you claim.
See,
. help weights
I recommend you to stay away from importance weights in most cases,
but they are very handy in the rare cases when you as a programmer
need them. For example, in my confidence ellipse program (. findit
ellip) I implemented Bartels' "fractional pooling of disparate
observations" in the pool(#) option using fractional importance
weights to downweigh "problematic" observations, e.g. problematic in a
Bayesian sense.
Why do you want to use importance weights?
Anders Alexandersson
"B. Burcin Yurtoglu" <burcin.yurtoglu@u...> wrote:
> The use of "importance" weights seems to increase the number of
> observations in the regress command.
>
> E.g.
>
> reg y x
>
> produces an output say with 8 observations
>
> whereas
>
> reg y x [iw=1/weight]
> produces an output with 17 observations
>
> Does anybody have an explanation or suggestions for reading?
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