Thomas Mayock <[email protected]>:
There are a number of relevant threads in the Statalist archives. You
may also want to read
http://www.econ.uiuc.edu/~roger/research/panel/long.pdf
or at least p.4, which states "In least squares applications the usual
strategy would be to transform y and X to deviations from individual
means, and then compute \hat{\beta} from the transformed data. For
quantile regression this decomposition of projections isn't available
and we are required to deal directly with the full problem."
You can't specify clustering for -qreg but -bootstrap- supports the
-cluster- option, if you are mainly worried about getting standard
errors slightly more robust to clustering. But I don't know how
-bootstrap- with the -cluster- option will perform in your data--you
may want to run simulations to see its small-sample properties in data
like yours.
On Thu, Jun 5, 2008 at 10:40 AM, Thomas Mayock <[email protected]> wrote:
> Dear Statalisters
> I am attempting to estimate a quantile regression model with many (over 100) geographic fixed effects. My initial attempt at estimation was to just include group indicator dummies. The drawback to this approach is computational cost: it takes several hours for the model to run.
> As the group fixed effects are of no interest in the study, I was thinking of performing the group-wise within transformation on the data, then implementing qreg. As I am new to quantile regression, however, I was unsure as to whether or not this is valid. Is anyone aware of other studies that have faced this issue or methodological papers that address a clustering structure in cross-sectional quantile regression?
> Any information is greatly appreciated.
>
> Tom
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