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Re: st: Computation Speed for Bootstrapping

From   Benjamin Volland <>
Subject   Re: st: Computation Speed for Bootstrapping
Date   Fri, 02 Dec 2011 12:08:02 +0100

Hi Danny,

your most easy option is to follow Jeph's second suggestion.
That is, run your estimation once on the full sample. Then use
- matrix b = e(b) - and then simply re-use the same matrix as starting
values for each bootstrap iteration, using the -from(b)- option in your -xtprobit- command.
You'll probably have to check whether this increases estimation speed
substantially using a small number of bootstrap interations, and simply compare the time with and without setting initial values. If you want to use different starting values in each iteration (though I somehow fail to see the improvement given that each bootstrap iteration simply draws a new sample from the original data), i.e. write your own bootstrap program, check the Sata FAQ from the UCLA on how to do it:
Again, whether that substantially increases the speed of your estimation you would have to check.

Best, Ben

On 01/12/2011 23:41, Danny Dan wrote:
Thank you so much for the answer Jeph. However, I do not know how to
write the code as mentioned in your reply. If you can give me further
insight that would be really helpful.

Thanks once again.

On Sun, Nov 27, 2011 at 10:06 AM, Jeph Herrin<>  wrote:
One trick is to re-use the estimation from each model as the starting values
for the next one. So after calling xtprobit, save the matrix e(b) and then
use it with the -from(b)- option.

   xtprobit ...
   matrix b=e(b)
   xtprobit ...., from(b)

But to do this, I think you'll have to write your own bootstrap code (not
too difficult).
Or you may get by with using -bootstrap:-, if you reuse the same matrix for

Hope this helps,

On 11/26/2011 10:31 PM, Danny Dan wrote:

Dear All,

How to increase computation speed while generating standard errors
using bootstrapping for panel data probit model (xtprobit,

Reduction in the number of replications (reps()) is of no help.

Please let me know.

Thank you,


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