### Stata FAQ How can I use -estout- to make regression tables that look like those in journal articles?

This FAQ illustrates the estout command that makes regression tables in a format that is commonly used in journal articles. The estout command was written by Ben Jann of ETH Zurich. You can download estout from within Stata by typing findit estout (see How can I use the findit command to search for programs and get additional help? for more information about using findit).

Let's illustrate use of the estout command using the high school and beyond data file.
use http://www.ats.ucla.edu/stat/stata/notes/hsb2, clear
(highschool and beyond (200 cases))
We will run 3 regression models predicting the variable read.  The first model will predict from the variables female and write; the second model will predict from female, write and math; and the third model will predict from female, write, math, science and socst. After each regress we will run an estimates store command. We will then use estout to create a single table that will summarize these models side by side.
regress read female write

Source |       SS       df       MS              Number of obs =     200
-------------+------------------------------           F(  2,   197) =   66.11
Model |  8401.94189     2  4200.97094           Prob > F      =  0.0000
Residual |  12517.4781   197   63.540498           R-squared     =  0.4016
Total |    20919.42   199  105.122714           Root MSE      =  7.9712

------------------------------------------------------------------------------
read |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
female |  -4.532084   1.171072    -3.87   0.000     -6.84153   -2.222637
write |   .7067537   .0616783    11.46   0.000     .5851192    .8283882
_cons |   17.40106   3.202315     5.43   0.000     11.08584    23.71628
------------------------------------------------------------------------------

estimates store m1, title(Model 1)

Source |       SS       df       MS              Number of obs =     200
-------------+------------------------------           F(  3,   196) =   68.34
Model |  10695.1896     3  3565.06321           Prob > F      =  0.0000
Residual |  10224.2304   196  52.1644406           R-squared     =  0.5113
Total |    20919.42   199  105.122714           Root MSE      =  7.2225

------------------------------------------------------------------------------
read |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
female |  -2.739657    1.09497    -2.50   0.013    -4.899092   -.5802214
write |   .3924361   .0732832     5.36   0.000     .2479114    .5369609
math |   .4753659   .0716952     6.63   0.000     .3339729    .6167589
_cons |   7.986659   3.230313     2.47   0.014     1.616025    14.35729
------------------------------------------------------------------------------

estimates store m2, title(Model 2)

regress read female write math science socst

Source |       SS       df       MS              Number of obs =     200
-------------+------------------------------           F(  5,   194) =   56.29
Model |  12383.6535     5   2476.7307           Prob > F      =  0.0000
Residual |  8535.76652   194  43.9987965           R-squared     =  0.5920
Total |    20919.42   199  105.122714           Root MSE      =  6.6332

------------------------------------------------------------------------------
read |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
female |  -1.328513   1.046793    -1.27   0.206    -3.393068    .7360424
write |   .1503085   .0778554     1.93   0.055    -.0032431    .3038601
math |   .2934723   .0728471     4.03   0.000     .1497983    .4371463
science |   .2508791   .0667312     3.76   0.000     .1192673     .382491
socst |   .2694578   .0574134     4.69   0.000     .1562232    .3826923
_cons |    2.44264   3.107255     0.79   0.433    -3.685698    8.570977
------------------------------------------------------------------------------

estimates store m3, title(Model 3)

estout m1 m2 m3

---------------------------------------------------
m1           m2           m3
b            b            b
---------------------------------------------------
female          -4.532084    -2.739657    -1.328513
write            .7067537     .3924361     .1503085
math                          .4753659     .2934723
science                                    .2508791
socst                                      .2694578
_cons            17.40106     7.986659      2.44264
---------------------------------------------------
Now we have a perfectly fine table that just includes the regression coefficients. We will modify the estout command to add standard errors and stars for statistical significance. We will also format the output so that coefficients will have three decimal places and the standard errors to two decimal places. Note, the par option for "se" places parentheses around the standard error.
estout m1 m2 m3, cells(b(star fmt(3)) se(par fmt(2)))

------------------------------------------------------------
m1              m2              m3
b/se            b/se            b/se
------------------------------------------------------------
female             -4.532***       -2.740*         -1.329
(1.17)          (1.09)          (1.05)
write               0.707***        0.392***        0.150
(0.06)          (0.07)          (0.08)
math                                0.475***        0.293***
(0.07)          (0.07)
science                                             0.251***
(0.07)
socst                                               0.269***
(0.06)
_cons              17.401***        7.987*          2.443
(3.20)          (3.23)          (3.11)
------------------------------------------------------------
The table is better now, but it can be improved further by putting the model names above the columns, adding a legend and by changing the label for "_cons" to "constant."
estout m1 m2 m3, cells(b(star fmt(3)) se(par fmt(2))) ///
legend label varlabels(_cons Constant)

--------------------------------------------------------------------
Model 1         Model 2         Model 3
b/se            b/se            b/se
--------------------------------------------------------------------
female                     -4.532***       -2.740*         -1.329
(1.17)          (1.09)          (1.05)
writing score               0.707***        0.392***        0.150
(0.06)          (0.07)          (0.08)
math score                                  0.475***        0.293***
(0.07)          (0.07)
science score                                               0.251***
(0.07)
social studies score                                        0.269***
(0.06)
constant                   17.401***        7.987*          2.443
(3.20)          (3.23)          (3.11)
--------------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001
Next, we want to add some things to the table, like R-squared, residual degrees of freedom and BIC. Stata has special names for each of these ancillary statistics, "r2" is the name for R-squared, "df_r" for residual degrees of freedom and "bic" for the BIC. You can get the names of these items from the ereturn list and from the help file.
estout m1 m2 m3, cells(b(star fmt(3)) se(par fmt(2)))  ///
legend label varlabels(_cons constant)              ///
stats(r2 df_r bic)

--------------------------------------------------------------------
Model 1         Model 2         Model 3
b/se            b/se            b/se
--------------------------------------------------------------------
female                     -4.532***       -2.740*         -1.329
(1.17)          (1.09)          (1.05)
writing score               0.707***        0.392***        0.150
(0.06)          (0.07)          (0.08)
math score                                  0.475***        0.293***
(0.07)          (0.07)
science score                                               0.251***
(0.07)
social studies score                                        0.269***
(0.06)
constant                   17.401***        7.987*          2.443
(3.20)          (3.23)          (3.11)
--------------------------------------------------------------------
r2                          0.402           0.511           0.592
df_r                      197.000         196.000         194.000
bic                      1410.783        1375.608        1350.106
--------------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001
Okay, we're almost done. We just need to clean up the lower part of the table giving each of the items a better label and adjusting the number of decimal places for each of the items.
estout m1 m2 m3, cells(b(star fmt(3)) se(par fmt(2)))   ///
legend label varlabels(_cons constant)               ///
stats(r2 df_r bic, fmt(3 0 1) label(R-sqr dfres BIC))

--------------------------------------------------------------------
Model 1         Model 2         Model 3
b/se            b/se            b/se
--------------------------------------------------------------------
female                     -4.532***       -2.740*         -1.329
(1.17)          (1.09)          (1.05)
writing score               0.707***        0.392***        0.150
(0.06)          (0.07)          (0.08)
math score                                  0.475***        0.293***
(0.07)          (0.07)
science score                                               0.251***
(0.07)
social studies score                                        0.269***
(0.06)
constant                   17.401***        7.987*          2.443
(3.20)          (3.23)          (3.11)
--------------------------------------------------------------------
R-sqr                       0.402           0.511           0.592
dfres                         197             196             194
BIC                        1410.8          1375.6          1350.1
--------------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001
We now have a table that's acceptable for publication in many journals. Of course, each periodical defines its own formats. Fortunately, estout is very flexible and has many options that will adapt to almost any periodical's requirements.

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