Custom statistics in nested regression tables

In recent version of asdocx (Jan 20, 2022: Version 1.9.8), the option stat() of nested regressions has a big improvement. Previously, option stat()would only report additional regression statistics that are stored in macro e(). The updated version of asdocx allows us to report several other descriptive statistics for the dependent or any other selected variable. In this post, I show several examples of how option stat() can be used in different scenarios. Please note that each statistic should be separated by the character comma.

Report statistics available in e() macro

After any regression model, you type ereturn list, you shall see different scalars / macros available on the screen. These can be reported in the nested regression table by writing the macro name inside option stat(). For example, after running an OLS model, the following macro are available.

 ereturn list 
                e(N) =  69
               e(df_m) =  2
               e(df_r) =  66
                  e(F) =  11.05650420530028
                 e(r2) =  .250961904598189
               e(rmse) =  2558.53561189829
                e(mss) =  144754063.3643494
                e(rss) =  432042895.5052159
               e(r2_a) =  .2282637804951038
                 e(ll) =  -637.8293069393488
               e(ll_0) =  -647.7986144493904
               e(rank) =  3

Let’s report the rmse and r2_a (the adjusted r-squared) in the nested regression model.

asdocx reg price mpg rep78, nest replace stat(rmse, r2_a)
Table: Regression results
mpg -271.643***
rep78 666.957*
Intercept 9657.754***
Observations 69
R2 0.251
RMSE 2558.536
Adjusted R2 0.228


Report custom-statistics

Descriptive statistics can be reported for any variable in the nested regression tables using options statvar(varname) and stat() together. If option statvar() is not used, the descriptive statistics are reported for the dependent variable of the regression model. The following descriptive statistics are allowed.

option details
N Number of observations
mean Arithmetic mean
sd Standard deviation
sum Sum / total
min The smallest value
max The largest value
skewness Skewness
kurtosis Kurtosis
p1 1st percentile
p5 5th percentile
p10 10th percentile
p25 25th percentile
p50 Median or the 50 percentile
p75 75th percentile
p90 90th percentile
p99 99th percentile

Let’s make a nested table of two regressions, each one having a different dependent variable. Also, let’s add mean, standard deviation, skewness, and kurtosis for the dependent variables.

asdocx reg price mpg rep78, nest replace stat(mean sd skewness kurtosis)
asdocx reg foreign mpg rep78 length turn , nest  stat(mean sd skewness kurtosis)
Table: Regression results
Variables (1) (2)
price foreign
mpg -271.643*** -0.021*
(57.771) (0.011)
rep78 666.957* 0.182***
(342.356) (0.045)
length -0.008**
turn -0.036*
Intercept 9657.754*** 3.072***
(1346.54) (0.79)
Observations 69 69
R2 0.251 0.58
Mean 6146.043 0.304
Std. Dev. 2912.44 0.464
Skewness 1.688 0.85
Kurtosis 5.032 1.723


Option statvar()

Option statvar() is used to specify the variable for which descriptive statistics are reported. The default variable is the dependent variable of the regression model.