Yes. The paper by King, Tomz and Wittenberg cited in your excerpted quote
explains simulation of MLE parameters in plain language, with examples. The
paper describes the simulation process step by step in two cases: when you
want expected values and when you want predicted values. Sounds to me like
it's exactly what you need.
Gabi
On Wed, Oct 23, 2013 at 11:23 AM, Mike Dylan <seancacti101(a)gmail.com> wrote:
Zelig package is what I have been looking for a long
time. I slight
problem is that I dont understand how sim() is run. I read through the
docs and I think it is written for graduate students. We can run the
numbers but at the end we need to be able to explain the results to the
audience.
Can somebody explain with an example, how does sim() work?
"Zelig simulates parameters from classical *maximum likelihood* models
using asymptotic normal approximation to the log-likelihood. This is the
same assumption as used for frequentist hypothesis testing (which is of
course equivalent to the asymptotic approximation of a Bayesian posterior
with improper uniform priors). See King, Tomz, and Wittenberg
(
2000)<http://gking.harvard.edu/files/abs/making-abs.shtml>ml>.
For *Bayesian models*, Zelig simulates quantities of interest from the
posterior density, whenever possible. For *robust Bayesian models*,
simulations are drawn from the identified class of Bayesian posteriors."
Regards,
Mike
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