There is a more general point which might get obscured
by references to -parmest-, -estout- and so forth.
It is that you can see more decimals by looking at
the saved results, using -return list- or -ereturn list-
as appropriate. In some cases the P-value is one
such saved result; in others you have do more work
to get at a P-value.
Programs such as -parmest- and -estout- do their magic
by seeking out such returned results.
A different kind of point is that once a P-value shows
that you are far out in the tail of a sampling distribution,
the accuracy (let alone exactness) of that P-value is
especially sensitive to whether the underlying
assumptions are indeed satisfied. Scientifically
it makes no difference to me, and no doubt at least
some others, what follows P = 0.0000,
as the only issues are then _interpreting_ a model
fit that passes every conventional significance
test.
Newson, Roger B
I personally aencounter this problem all the time. My usual solution is
to use the -parmest- package, downloadable from SSC, which produces an
output dataset (or resultsset) with one observation per parameter and
data on estimates, confidence intervals, P-values and other parameter
attributes. This dataset can be listed to the log and/or saved to disk
and/or written to memory, overwriting the existing dataset.
For instance, you might type
regress mpg weight foreign
parmest, list(parm estimate stderr t min96 max95 p) format(p %-8.2g)
and get the results listed with the P-value in a left-justified format,
usually with 2 significant figures. The on-line help for -parmest-
contains many more references to other tricks that can be done with
-parmest-, together with other packages on SSC. So does my website (see
my signature below).
Alternatively, there is probably a solution with either -estout- or
-outreg-, which you can also download from SSC.
Marcus Fischer
is there a way to report p values with more than 4 decimals ?
(in case of low p values STATA reports 0.0000). I would like to know the
exact p value.
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