I have to evaluate the finite-sample performance of the ML estimator
in the estimation of a 3-parameter distribution, based on simulated
samples.
The "progressive loop" environment which is set for Monte Carlo
studies does not seem to support the mle command. I ended up using
the index loop which supports the mle command, while writing the
coefficient vector obtained in each loop that is grabbed by $coeff,
to successive rows of a pre-defined zero matrix. So far so good.
The problem is the following: the distribution appears difficult to
estimate in small samples and rather often the convergence criterion
for the mle is not met (I don't mind that, this is part of the
evaluation of the performance of the MLE). But when this happens, the
loop stops. The total number of runs of the loop will
possibly require some hours to run. If the loop stops every time the
mle does not converge, I will have to be constantly overseeing the
process for the duration, in order to re-ignite the loop. It would
be very convenient if the loop could continue irrespective of
whether the mle in a particular loop fails to converge.
Is there a way to tell Gretl not to stop the loop, even if the mle
fails, to write the container matrix with NANs, and to proceed to
the next run;
Thank you.
--
Alecos Papadopoulos
Athens University of Economics and Business, Greece
Department of Economics
cell:+30-6945-378680
fax: +30-210-8259763
skype:alecos.papadopoulos