In our introductory article on using `Outreg2`

for regression output, we learnt how to output Stata regression output into other file formats like Word, Excel or Latex and how we could adjust the layout of the output tables. In this second section of our two-part article on the * outreg2* command, we explore how additional statistics, beyond the default output, can be reported. To read part 1 of the article on the

*outreg2*command, click here.

*Reporting Adjusted R-squared in regression output*

*Reporting Adjusted R-squared in regression output*

To enhance the usefulness of the command, * outreg2* also comes with certain options to customise the statistics we need it to report. If, for example, we wish to report the adjusted R-squared (instead of R-squared), we use the

*option.*

`adjr2`

regress price mpg headroom trunk displacementoutreg2 using results, word replace adjr2

If we do not want any of the R-squared value reported, we specify the * nor2* option:

outreg2 using results, word replace nor2

*Removing Number of Observations*

*Removing Number of Observations*

Be default, the output table shows the number of observations for each regression column. If we do not want the number of observations reported, the option of * noobs* removes the statistic from the table:

outreg2 using results, word replace noobs

*Sideways Reporting of Standard Errors*

*Sideways Reporting of Standard Errors*

The option * sideway* reports the standard errors in a separate column beside the regression coefficients instead of underneath them.

outreg2 using results, word replace sideway

To further remove parentheses from these sideways standard errors, we add the * noparen* option:

outreg2 using results, word replace sideway noparen

*Excluding Standard Errors*

*Excluding Standard Errors*

If we do not want standard errors to be reported at all, we just specify the * nose* option.

outreg2 using results, word replace nose

*Reporting Additional Statistics: *`addstat`

*& *`stat`

*Reporting Additional Statistics:*

`addstat`

*&*

`stat`

By default, * outreg2* outputs the coefficients, their standard errors, R-squared and the number of observations in the table. To add more statistics, we need to specify an option called

*with the names and macros of each new statistic that we require typed inside the parenthesis. For example:*

`addstat()`

outreg2 using results, word replace addstat("F-Stat",e(F),"Prob > F",e(p),"Degree of Freedom",e(df_r))

To explain the syntax inside the * addstat()* parenthesis, let’s digress a little into scalars. After every regression, Stata stores values of different statistical measures in temporary variables called scalars. These variables get populated with certain regression output statistics each time we run a regression. For example, e(N) stores the number of observations, while e(r2) stores the R-squared of the regression that was previously run. A list of these scalars and what they store can be accessed by running the command:

ereturn list

Or a comprehensive list of them is also present in Stata’s documentation for the `regress`

command which can be read under “Stored Results” via:

help regress

In our example, we make use of these scalars and report the F-stat stored in e(F), Prob > F stored in e(p), and degrees of freedom stored on e(df_r). Stata is then able to refer to the values stored in these scalars and output them in the table.

Several other statistics can be reported using the * stat()* option. These include, among others, t-stats, p-values, confidence intervals, and standardised beta.

outreg2 using results, word replace stat(coef beta se ci) sideway

The option of * sideway* ensures that these statistics are reported in a separate column each.

*Specifying Confidence Intervals and Significance Levels*

*Specifying Confidence Intervals and Significance Levels*

By default, Stata sets the confidence intervals at 95% for every regression. To have specific levels of confidence intervals reported, we use the * level()* option.

outreg2 using results, word replace stat(coef ci) sideway level(90)

Significance levels can also be similarly specified. By default, Stata reports significance levels of 10%, 5% and 1%. We can further tailor this through the * alpha()* option:

outreg2 using results, word replace stat(coef tstat) sideway alpha(0.01, 0.05)

The above command would report only the 1% and 5% significance levels. No asterisk will be added for a 10% significance level.

If we wish to specify the number of asterisks to be reported for each significance level, we follow the * alpha()* option with the

*option:*

`symbol()`

outreg2 using results, word replace stat(coef tstat) sideway alpha(0.01, 0.05) symbol(***, **)

This ensures that two and three asterisks are used to symbolise significance levels of 5% and 1% respectively. Stata adjusts its note at the bottom of the table accordingly.

If we desire for the asterisks to appear beside t-stats instead of the coefficients, we specify * asterisk(tstat)* as an option.

outreg2 using results, word replace stat(coef tstat) sideway alpha(0.01, 0.05) symbol(***, **) asterisk(tstat)

Or we can remove the asterisks altogether using the * noaster* option

outreg2 using results, word replace noaster

*Keeping/Dropping Variables from the Output Table*

*Keeping/Dropping Variables from the Output Table*

If we only wish to retain some of the independent variables in our output table of regressions, we can use the * keep()* option to specify (in the parenthesis) which of these variables we want reported. Alternatively, we can use the

*option to indicate (in the parenthesis) which ones we want omitted. This comes in particularly useful when we have fixed effect dummy variables in our regression models.*

`drop()`

regress price mpg headroom trunk displacementoutreg2 using results, word replace keep(mpg trunk)

OR

outreg2 using results, word replace drop(headroom displacement)

So, even though our regression was performed using four independent variables, the * keep()* or

*options ensure that the output table only has results for ‘mpg’ and trunk‘.*

`drop()`