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RE: st: Bootstrap with time series models


From   ROBERTO PASCUAL <rpascual@uib.es>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: Bootstrap with time series models
Date   Thu, 04 Oct 2007 10:21:04 +0200

Dear Robert and Austin,
Thanks a lot for your useful comments!
Robert

___________________________________________________________

-----Mensaje original-----
De: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Austin Nichols
Enviado el: miércoles, 03 de octubre de 2007 23:14
Para: statalist@hsphsun2.harvard.edu
Asunto: Re: st: Bootstrap with time series models

ROBERTO PASCUAL <rpascual@uib.es>:
Bob Yaffee refers to
http://www.stata.com/statalist/archive/2007-08/msg01207.html
which code would take quite a lot of adapting for your use.

I haven't read:
Politis, D.N., Romano, J.P. and Wolf, M. (1999). Subsampling, Springer, NY.
Lahiri, S.N. (2003). Resampling Methods for Dependent data, Springer, NY.
Politis, D.N. and Romano, J.P. (1994). The Stationary Bootstrap. JASA,
89:1303-1313.
but:
I suspect both subsampling and the block bootstrap are easy to
program: for the block bootstrap you just select _N/L observations
(from the first _N-L+1 obs) and the L-1 obs that follow them in each
iteration, then stitch them together, right (where the block length L
for each iteration is random and generated from a geometric
distribution)?  Perhaps an enhancement of the -gsample- program could
do it?  That would be up to its author, Ben Jann, of course...

The non-overlapping block bootstrap is already possible with the
cluster option.  Just define the cluster variable as blocks of time...

On 10/3/07, Robert A Yaffee <bob.yaffee@nyu.edu> wrote:
> Time series models require a block or wild bootstrap.
> Stata does not have either of these yet, though I think that Austin
Nichols indicated that
> there is a wild bootstrap in workshop somewhere.  I think he said that it
needs documentation.
>
> ----- Original Message -----
> From: ROBERTO PASCUAL <rpascual@uib.es>
> >
> > Is there any way to use the Stata bootstrapping facility with time
series
> > estimation functions, such as var or arima?
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