If we want to distinguish between true NAs and nan/inf (as we
probably should), some other design questions come up, as a
consequence of the fact that we would be allowing non-finite
values in series and scalar variables. (Unless, that is, we make
it an error to put non-finite values into such variables.)
I presume that in simple, per observation, calculations such as y
= log(x) or y = x*z we'd want to let IEEE rules prevail, but what
about more complex calculations?
At present we automatically exclude observations with NAs from
regression calculations, means and variances and so on. Should we
do the same for nan/inf, or should we let IEEE rules prevail -- or
should we add a "set" switch to control this?
A practical use case is this:
series lx = log(x)
ols y 0 lx
where the series x contains non-positive values. Right now the bad
log x values are converted to NA and skipped. If we leave them as
nan or -inf then what should we do?
Allin