quantile regression fits a line at different points of the CONDITIONAL
distribution of y given x, i.e. f(y|x).
At each fixed value of x (i.e. x=x0) f(y|x=x0) gives you the
distribution of y after controlling for x (a distribution of the
errors in other words). Qreg fits a line at a given (prespecifed)
quantile of such distribution.
If you're not including variable z as a regressor in the conditioning
set then qreg won't give you what you want as stated in your goal (
"coefficients of experience in a series of wage regressions by
quantile of the distribuiton of ability").
If you're willing to assume exogeneity on your excluded variable z, it
seems to me that it would suffice to just split the sample by
quantiles of the excluded variable and run an ordinary regression over
each sample (a fully interactive model in other words).
robert
On 7/25/05, Antonio Fanari <[email protected]> wrote:
> Hi,
> is it possible to run quantile regressions where the quantiles are not
> defined over the independent variable but over some other variable?
> Say you have wages as a dependent variable and another variable measuring
> ability (e.g. an IQ test). Suppose you want to see if the effect of, say,
> experience on wages varies across quantiles of ability. One way is to
> include interactions of experience*ability in a traditional OLS regression.
> However, I was wondering if there is a way to run quantile regressions,
> instead. So one would come up with coefficients of experience in a series of
> wage regressions by quantile of the distribuiton of ability.
> Please forgive me if the question does not make any sense, but I would
> appreciate any feedback!
> Thanks.
> Tony Fanari
>
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