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