st: RE: post estimation tests with areg

["Martin Weiss" <martin.weiss1@gmx.de>]

<>


"I am a novice Stata user (of 2 weeks) with more questions than answers."


BTW, Ben, a very warm welcome to you, it is very good to have you on board!
Do not let initial frustration put you off, as hard as it may be sometimes.
Stata is big fun once you get past the initial problems!



*************
h areg post
*************

gives you all the post-estimation options after -areg-. In the upper right
corner, you will find a blue link to the predict dialog: Having run -areg-
previously, you can let the dialog box guide you in your choice of syntax...



HTH
Martin


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Benhoen2
Sent: Dienstag, 21. September 2010 19:15
To: statalist@hsphsun2.harvard.edu
Subject: st: post estimation tests with areg

Hello Stata-listers,

I am a novice Stata user (of 2 weeks) with more questions than answers.  I
am using the areg command to efficiently control for ~2000 fixed effects
variables in a regression that has 3-7 independent variables for ~ 110,000
cases.  Although the regression itself is very useful (and very fast), I
have been unsuccessful at finding a way to do the following post-estimation
activities:

1) save predicted values and residuals:  I have tried using predict xp
[varname] and predict r [varname], respectively. Both generate the following
error, "too many variables specified"
2) save standardized residuals (Though if I had un-standardized residuals I
could calculate myself)
3) test for heteroskedasticity
4) produce VIF statistics among IV, and
5) produce leverage statistics

...essentially many of the post-estimation options from regress.  Are there
any programs out there to produce these?  (A SSC search was unsuccessful)  

Should I be using another regress command entirely? Is regress the only way
to get there?

If it turns out that regress is the only way to handle this, any advice on
a) how to efficiently create the 2000 fixed effects variables, and b)
encourage the most efficient use of regress with these variables.

Thanks, in advance, for any and all.

Ben
Berkeley Lab


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