Hi, Pureum.
you should choose the cutpoints with the aim to get the "best" match. I imagine
this means:
a) maximize the number of matched treated units (your point 2)
b) minimize residual imbalance
of course a) and b) are on a trade-off.
So: non problem in using deciles for some covariates if you think that the imbalance you
are leaving in those variables is in fact tolerable
3. i've no threshold value for L1.
i simply use L1 to compare the imbalance of raw data to the imbalance of matched samples,
or to compare the imbalance obtained in matched samples obtained with different covariates
or different cutpoints
4. if your question is about how to run the regression after CEM, i think you are right:
it's not a problem to run the regression even if some treated units are matched to
several control units, if you run the regression using cem weights
All the best
Beppe
Il 23/04/15 15:15, pureum kim <pureum.kim(a)usc.edu> ha scritto:
Dear CEM List members,
My research question examines whether firms adopting fair value accounting for their
pensions change their pension plan assets. I was wondering if you could help me with the
following
quick questions about Coarsened Exact Matching.
1. What is the best practice for selecting the cut points? (I have read that Scott and
Freedman-Diaconnis are the preferred methods.)
2. My treatment sample is very small and using Scott or Freedman-Diaconnis leads me to
lose many treated firms. So I use deciles for certain matching variables instead of the
Scott or FD. Would this still be a concern if the imbalance is low?
3. Is there a reasonable value for L1? I know that the lower value means less imbalance
but is there a rule of thumb?
4. After creating a matched sample, are there any practical guides on how to run the
regression? Some treated firms are matched with multiple control firms, and I believe that
it is not a problem to run the regression even if some firms are matched with more control
firms. Is there an issue if we don't have exact number of paired treated and
controlled firms?
Thank you so much for your time and consideration.
Kind Regards,
Pureum Kim
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