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Re: st: RE: Why no RMSE in -ereturn list- after -areg-?


From   "Clive Nicholas" <[email protected]>
To   [email protected]
Subject   Re: st: RE: Why no RMSE in -ereturn list- after -areg-?
Date   Thu, 2 Dec 2004 03:00:04 -0000 (GMT)

Nick Cox replied:

> Why? I guess a developer thought it redundant,
> or didn't think it important, or forgot, or something.
> How about
>
> . di sqrt(e(rss)/e(df_r))
>
> or
>
> . di sqrt($S_E_sse/$S_E_tdf)

I was able to replicate this with another (-wagepan-) dataset...

. areg lwage exper married south union d82 d83 d84 d85 d86 d87, absorb(nr)

                                                     Number of obs =    4360
                                                     F( 10,  3805) =   77.96
                                                     Prob > F      =  0.0000
                                                     R-squared     =  0.6160
                                                     Adj R-squared =  0.5601
                                                     Root MSE      =  .35324
----------------------------------------------------------------------------
     lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------+----------------------------------------------------------------
     exper |   .1130036   .0214828     5.26   0.000     .0708846    .1551227
   married |   .0577281   .0183605     3.14   0.002     .0217307    .0937254
     south |   .1107554   .0482741     2.29   0.022     .0161099    .2054009
     union |   .0843624   .0194332     4.34   0.000     .0462618     .122463
       d82 |  -.0598641   .0370691    -1.61   0.106    -.1325414    .0128131
       d83 |  -.1301425   .0566215    -2.30   0.022    -.2411539   -.0191311
       d84 |  -.1748378   .0771744    -2.27   0.024    -.3261449   -.0235307
       d85 |  -.2391411   .0981043    -2.44   0.015    -.4314832    -.046799
       d86 |  -.2937814   .1192146    -2.46   0.014    -.5275121   -.0600506
       d87 |  -.3463798   .1404224    -2.47   0.014    -.6216903   -.0710694
     _cons |   .9837128   .0777398    12.65   0.000     .8312972    1.136129
-----------+----------------------------------------------------------------
        nr |      F(544, 3805) =      8.974   0.000         (545 categories)

. di sqrt(e(rss)/e(df_r))
.35323616

...but not for my own model...

. areg ldmch el1983-el2001 c_difftout c_enp incumb c_normcspd c_normlspd
  c_normdspd c_sqldmspd c_cdmargin c_ldmargin c_class ldmseats if !inlist
  (_n, 379) [pw=weight], absorb(pano) cluster(region)

Regression with robust standard errors               Number of obs =    3347
                                                     F( 16,    21) =  478.30
                                                     Prob > F      =  0.0000
                                                     R-squared     =  0.7757
                                                     Adj R-squared =  0.7276
                                                     Root MSE      =  3.7027

                         (standard errors adjusted for clustering on region)
----------------------------------------------------------------------------
           |               Robust
     ldmch |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------+----------------------------------------------------------------
    el1983 |   11.06352   .7317441    15.12   0.000     9.541772    12.58526
    el1987 |   4.070268    .889115     4.58   0.000     2.221252    5.919284
    el1992 |   1.856636   1.095025     1.70   0.105    -.4205945    4.133866
    el1997 |   3.110942   1.094698     2.84   0.010     .8343927    5.387491
    el2001 |   6.117755   1.414653     4.32   0.000     3.175823    9.059687
c_difftout |   .0439656   .0249076     1.77   0.092    -.0078325    .0957638
     c_enp |   2.263927   .7102512     3.19   0.004     .7868788    3.740975
    incumb |  -.5054217   .2286173    -2.21   0.038    -.9808573   -.0299861
c_normcspd |  -2.735679   .7467913    -3.66   0.001    -4.288716   -1.182641
c_normlspd |  -4.763069    .774382    -6.15   0.000    -6.373485   -3.152654
c_normdspd |   1.946096   1.479461     1.32   0.203    -1.130613    5.022804
c_sqldmspd |   5.467829   1.280315     4.27   0.000     2.805268     8.13039
c_cdmargin |   .2592689   .0303518     8.54   0.000     .1961488     .322389
c_ldmargin |   .1967372   .0247946     7.93   0.000      .145174    .2483003
   c_class |  -.0103208   .0342645    -0.30   0.766    -.0815777    .0609361
  ldmseats |   5.124764    .757683     6.76   0.000     3.549076    6.700451
     _cons |  -4.274048   .7438351    -5.75   0.000    -5.820938   -2.727158
-----------+----------------------------------------------------------------
      pano |   absorbed                                     (576 categories)

. di sqrt(e(rss)/e(df_r))
42.40995

The reason is because I switched the -cluster()- option on in my model.
Switching it off and then running that calculation -display-s the RMSE.
I've no idea why the option makes it return a completely different number.

Notice also that the first model produces F statistics on the fixed
effects. Now I've never seen that reported before in -areg-, for it
certainly hasn't appeared in any of my models: it simply says 'absorbed'
every time. Generating dummies for my -pano- variable didn't do the trick

CLIVE NICHOLAS        |t: 0(044)7903 397793
Politics              |e: [email protected]
Newcastle University  |http://www.ncl.ac.uk/geps

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