Davide Cantoni <[email protected]> :
You don't say how many more obs you want--let's assume you want about
100 times as many:
expand 100
will do it, or
g u=round(uniform()*200)
expand u
for a random-sized sample about 100 times as big with the same DGP.
You could also
loc n=_N*100
g u=round(uniform()*1000)
expand u
drop u
g u=uniform()
sort u
drop if _n>`n'
for a sample 100 times as big but with random numbers of replications
of each obs.
On Sun, Jun 21, 2009 at 11:58 PM, Davide Cantoni
<[email protected]> wrote:
>
> Hello, I am stuck while thinking about this issue and I would
> appreciate your suggestions. I have a dataset which I use for
> simulation purposes, to test whether my do-files run correctly. The
> issue is that this dataset is too short for many applications, as it
> has only 200 observations.
>
> What I want to do is expand this dataset to include more observations,
> but keeping the same (unknown to me) data generating process that
> created the first 200 observations. So I was thinking to proceed in a
> bootstrapping manner, by drawing the values for each one of the
> variables (var1, var2 etc etc) for the new observations from the
> empirical distributions of var1, var2,... in the first 200
> observations. Yet, I have no idea on how to implement this. I'm
> grateful for any idea. Thanks for your interest,
>
> Davide
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