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# st: Computing the proportion of significant variables after running numerous regressions

 From George Murray To statalist@hsphsun2.harvard.edu Subject st: Computing the proportion of significant variables after running numerous regressions Date Sun, 13 May 2012 22:06:42 +1000

```Dear Statalist,

I am using the -foreach- command to run approximately 1000
(bootstrapped) regression models, however I require an efficient way
of calculating the proportion of the regression models which have a
statistically significant constant at the 5% level; and of the
constants which are statistically significant, the proportion which
are positive.  Below each of the 1000 regressions I run, a table is
displayed with the following format:

---------------------------------------------------------------------------------------------------
|    Observed                         Bootstrap
V0 |       Coef.             Bias         Std. Err.
[95% Conf. Interval]
-------------+------------------------------------------------------------------------------------
V1 |   .00968169  -.0000537   .00057051     .008721   .0111218  (BC)
V2 |  -.00110469   .0000782     .000691   -.0023101    .000459  (BC)
V3 |   .00468313  -.0001562   .00084971    .0031954   .0064538  (BC)
_cons |  -.00076976   .0001811   .00176677   -.0044496   .0025584  (BC)
--------------------------------------------------------------------------------------------------

I would be *very* grateful if someone knew the commands which would
allow me calculate this. In the past, I have used (a highly tedious
and embarrassing approach on) Excel where I filtered every Nth row,
and wrote a command to display 1 if the coefficient lies within the
confidence interval, and 0 if not. This time, however, I am running
numerous models and require a quicker approach.

One more question -- is there a way to create a new variable where the
coefficients of V1 (for example) are saved, so I can calculate the
mean, standard deviation etc.of V1?

If someone could answer at least one of these two questions, it would
be very much appreciated.

George Murray.
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```