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# st: Compare regression outputs of different models

 From iank131 <[email protected]> To [email protected] Subject st: Compare regression outputs of different models Date Thu, 11 Aug 2011 15:15:32 +0800

```Hello,

I am still new to Stata and StataList. I am using Stata10. I have two questions:

1. I am running a robust regression (rreg) and have several but
slightly different models to run on the same data set. I want to
compare the different models by lining them up vertically side by
side, but Stata gives the output model by model and then you need to
cut and paste the desired details from this output to another
application. However, I would like to find a quick way to save these
outputs and then generate a table in Stata in which I can see the see
the coefficients of each independent variable, their corresponding
standard errors below (not beside) the coefficient, and a star or
stars next to the coefficient if its significance is below say 10%,
5%, 1%, etc. Please see my example below.

2. How do you get stata to automatically calculate the r-squared and
adjusted r-squared after running rreg? It seems you need to manually
calculate it because there is no option for it in the command. I would
also like to save this in my generated table described above.

For example:

use auto
* Model 1
rreg price mpg weight, nolog
* Model 2
rreg price mpg weight length, nolog
* Model 3
rreg price mpg weight length foreign, nolog

My desired output in one table generated by Stata is:

price                        Model 1                Model 2
Model 3

mpg                        -50.5272               -48.0738
-23.87354
(41.58937)            (40.42094)
(65.77032)

weight                    .1032098              -1.014287*
3.28141***
( .3095952)            (.5621602)
( .9267939  )

length                                                    37.41137*
-27.0968

(19.12697)                (30.63898)

foreign
3346.348***

(597.8505 )

_cons                    5747.529***            1996.01
480.0023
(1736.373)             (2836.495)
(4781.245)

r-squared                   x                            xx
xxx

yyy

* p<.1
** p<.05
*** p< .01

Thanks,

Ian Kwan
PhD Candidate
University of Navarra

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```