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