st: r-squared in rreg output

[Kit Baum <baum@bc.edu>]

It is quite interesting that OLS and rreg come up with very similar  
r^2 measures, but coefficient estimates VERY different:

. reg price mpg rep78 headroom

      Source |       SS       df       MS              Number of obs  
=      69
-------------+------------------------------           F(  3,    65)  
=    7.51
       Model |   148497605     3  49499201.8           Prob > F       
=  0.0002
    Residual |   428299354    65  6589220.82           R-squared      
=  0.2575
-------------+------------------------------           Adj R-squared  
=  0.2232
       Total |   576796959    68  8482308.22           Root MSE       
=  2566.9

------------------------------------------------------------------------ 
------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf.  
Interval]
------------- 
+----------------------------------------------------------------
         mpg |  -289.3462   62.53921    -4.63   0.000    -414.2456    
-164.4467
       rep78 |   670.8971   343.5213     1.95   0.055     
-15.16242    1356.957
    headroom |  -300.0293   398.0516    -0.75   0.454     
-1094.993    494.9346
       _cons |   10921.33   2153.003     5.07   0.000      
6621.487    15221.17
------------------------------------------------------------------------ 
------


Robust regression                                      Number of obs  
=      69
                                                       F(  3,    65)  
=    7.40
                                                       Prob > F       
=  0.0002

------------------------------------------------------------------------ 
------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf.  
Interval]
------------- 
+----------------------------------------------------------------
         mpg |  -113.8438   26.05671    -4.37   0.000    -165.8826    
-61.80491
       rep78 |   464.9123   143.1268     3.25   0.002      
179.0684    750.7562
    headroom |  -176.2006   165.8466    -1.06   0.292     
-507.4191    155.0179
       _cons |   6395.867   897.0398     7.13   0.000      
4604.355    8187.379
------------------------------------------------------------------------ 
------


The interesting thing is that the weights derived by -rreg-, which  
are designed to lie in the unit interval, essentially throw out the  
data on the high-priced cars (almost every car with a price >= 9300  
is given a weight of zero). Nevertheless, the procedure is not quite  
the same as running OLS on that subsample in terms of the point  
estimates.

Kit Baum, Boston College Economics
http://ideas.repec.org/e/pba1.html


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