I am confronted with a Logit-regression, in which y=0 is much less frequent
than y=1. It is argued that the less frequent observations with y=0 should
receive higher weights in the regression, such that the proportion is
balanced between Ys being 0 and 1.
To my knowledge there are usually two motivations to use weights others than
unity:
- prior knowledge of the probability of y=0
- optimisation of a cost function (in the example above y=0 is much more
expensive and should be predicted with higher attention)
In my limited econometric library and in the internet I wasn't able to find
a discussion on the issue of weighting observations. If someone has a good
hint to a source or could sketch the ideas of consequences, pros and cons I
would be very pleased.
Thank you,
Thomas
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