Thank you Iain. Your explanation has almost trivialized my questions
regarding, ("what is the likelihood that value of beta doesn't fall within
the range of std. error?"). I will follow up with the rest during office
hours.
Thank you for your help.
Yousuf
On Sun, Mar 7, 2010 at 12:00 PM, <gov2001-l-request at lists.fas.harvard.edu>wrote:
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> Today's Topics:
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> 1. estimating fundamental and estimation uncertainty (Yousuf)
> 2. Re: estimating fundamental and estimation uncertainty
> (Iain Osgood)
> 3. replication software (Maya Sen)
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>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 6 Mar 2010 20:10:48 -0500
> From: Yousuf <usuf.marvi at gmail.com>
> Subject: [gov2001] estimating fundamental and estimation uncertainty
> To: gov2001-l at
lists.fas.harvard.edu
> Message-ID:
> <27c64f031003061710p21e4bea5u3bde7deea0f5312e at mail.gmail.com>
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>
> Dear Iain, Maya, and Professor King:
>
> >From what I understand from the class lecture is that we use a chosen
> parametric value, given the MLE estimate that we get from our model, to
> generate some simulations (lets say 1000) of our systematic and stochastic
> variables; correct?
>
> My question, given my understanding, is that why do we need to reduce
> uncertainty by increasing the number of simulations, if this uncertainty is
> recorded by the standard error and the error term. Additionally, I
> understand that the std.error is associated with one value of the
> systematic
> parameters, and hence, if we get a different a better estimate of those
> parameters, we would then get a different value for std. errors. However,
> what is the likelihood that value of beta doesn't fall within the range of
> std. error?
>
> Regards
>
> --
> Yousuf
>