Dear Maarten
Thank you. I am bringing over a the 'new hires'
variable from the employee dataset to the employer
dataset. That is, I wanted all establishments that
hired 5 or more people. Otherwise, I would have
establishments that hired 0, 1s' etc.
So I did
.collapse sum newhires, by (firmid)
keep if > = 5
I then merged it to the main file.
But the results had missing F and Prob>F.
I do not get missing F statistics if I do not just
take greater tha or equal to 5 hired. No standard
errors are missing. No variables are dropped. Some of
the estimates are small; but they may be "meant" to be
small.
Many thanks in advance
Maarten's quote:
It is not clear to me what you have done. What do you
mean by restrict:
are you imposing a constraint, or are you estimating
something on a
sub-sample, or are you doing something else? It helps
if you tell us
exactly what you typed. A lot of the problems are
caused by tiny
things, so if you don't tell us exactly what you
typed, we won't be
able to spot those. Do you get normal results if you
don't retrict
(whatever that may mean)? Are any (or all) of the
standard errors also
missing? Are any variables dropped? Are some of the
estimates unusually
large (or small)? If you are estimating a model on a
subsample, you
should have a good look at the variables in that
subsample: it may be
that a dumy variable consists almost entirely of zeros
(or ones) in a
subsample, which may cause problems, or there may be
much higher
multicolinearity in the subsample than in the entire
sample.
-- Maarten
--- Renuka Metcalfe <[email protected]> wrote:
> Dear Statalist
>
> I tried to run a simple OLS, with a merged employee
> and employer dataset, but I restricted one variable
> from the employee dataset to establishments who
hired
> 5 employees or more employees, whilst all employees
> are normally in the raw survey if they have less
than
> or equal to 25 employees or 25 employees are
randomly
> selected if they have > 25 employees.
>
> After running a simple OLS, the F (16, 1092) appear
to
> blank along with prob>F (see below).
>
> Regression with robust standard errors
> Number of obs = 1110
>
> F( 16, 1092) = .
>
> Prob > F = .
>
> R-squared = 0.6248
>
> Root MSE = .09573
>
>
> I would be grateful, if anyone would tell me why
this
> is the case.
>
> Thanks in advance
>
>
___________________________________________________________
Yahoo! Mail is the world's favourite email. Don't settle for less, sign up for
your free account today http://uk.rd.yahoo.com/evt=44106/*http://uk.docs.yahoo.com/mail/winter07.html
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
