I would like to pose some questions about the best way of dealing
with missing values when using mle. I had assumed, wrongly, that mle
simply applies case-wise deletion to cases with missing
values. Instead it terminates with a more or less informative error
message. On the other hand, built-in estimation commands seem to
handle missing values internally.
A. Is this the right thing to do? Deleting cases with missing
values in calculating the log-likelihood, etc is not difficult, but
there may be models when missing values cause problems if not dealt
with properly - eg any lag structure. Would it be possible to
provide an option (--delmiss) for case-wise deletion of missing values?
B. In writing a function it is not difficult to test for missing
values, but then what should the function do? Here the question is
what are the consequences of adjusting the sample to exclude cases
with missing values within the function? Would this affect the
sample used in the program that calls the function? If so, one would
really need a "smpl --restore" command to set the sample back to its
state on entry to the function.
C. Finally, the present arrangement can be maintained, in which case
I think that more consistent error messages should be generated by
mle if that is possible.
Gordon