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collect layout + Correlated random-effects regression

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Hi,

based on the following example, my understanding is that CRE _r_b and _r_se should be reported allined with other estimators, as they are fully comparable.

Code:
use http://fmwww.bc.edu/ec-p/data/greene2000/TBL15-1.dta,clear
xtset firm year
xtdes
vl clear
vl create depVar = (i)
vl create vlcont = (f c)
xtreg i f c,fe
estimate store fe
xtreg i f c,re
estimate store re
xtreg i f c,cre
estimate store cre
collect clear
collect create output
collect set output
qui etable, estimates(re fe cre ) cstat(_r_b,nformat(%3.2f)) showstars showstarsnote name(output, replace)
collect label levels etable_depvar 1 "re" 2 "fe" 3 "cre", modify
collect style cell result[_r_b] , nformat("%3.1f")
collect layout (coleq#colname#result[_r_b _r_se] result[N]) (etable_depvar#stars) (), name(output)

Collection: output
      Rows: coleq#colname#result[_r_b _r_se] result[N]
   Columns: etable_depvar#stars
   Table 1: 17 x 6

----------------------------------------------------------
                            re          fe         cre    
----------------------------------------------------------
f                           0.1 **      0.1 **            
                        (0.015)     (0.016)               
c                           0.3 **      0.3 **            
                        (0.024)     (0.024)               
Intercept                 -60.3       -62.6 *             
                       (54.484)    (29.442)               
f                                                   0.1 **
                                                (0.016)   
c                                                   0.3 **
                                                (0.024)   
Intercept                                          -2.1   
                                               (86.732)   
f                                                   0.3   
                                                (0.164)   
c                                                  -1.9   
                                                (1.067)   
Number of observations      100         100         100   
----------------------------------------------------------
** p<.01, * p<.05
LP

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