Thanks for the help. I think I could have been more specific
about exactly what I am trying to do.
I have 20 years of time series, which I have detrended.
What I want to do is to:
a) Take the detrended time series and estimate a kernel
density
b) Use the estimated kernel density to estimate the expected
value of shortfalls from the mean of the distribution. That
is, I want to calculate the integral of:
(Mu-Yt)dF(Yt)dYt, for all Yt<Mu
Where Mu is the mean, Yt is the random variable, and F(Yt)
is the cumulative density function of Yt.
The command kdensity returns the estimated density at
whatever points were used to estimate the density, and I can
add more points using the at() option. But these will leave
gaps at the omitted points in the support of the estimated
distribution when I sum up the densities. What I'm
wondering is if there is a way to integrate under the curve
of the estimated kernel density. Perhaps some stata
programming is in my future, but if there is way to do this
while using kdensity or something similar, I would love to
know. Thanks in advance.
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