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 search estout (see How can I use the search command to search for programs and get additional help? for more information about using search).
Let’s illustrate use of the estout command using the high school and beyond data file.
use https://stats.idre.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
-------------+------------------------------ Adj R-squared = 0.3956
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)
regress read female write math
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
-------------+------------------------------ Adj R-squared = 0.5038
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
-------------+------------------------------ Adj R-squared = 0.5815
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
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.