How to store the beta/standardized coefficients after a regression

[Victor Smith]

Is there a way to store the beta/standardized coefficients after a regression?

For example, how can I store the standardized coefficient of mpg to a local macro, which is -.1702392, for further use?

cls
clear all
webuse "auto.dta", clear

regress price mpg weight length, beta


// the results

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(3, 70)        =     12.98
       Model |   226957412         3  75652470.6   Prob > F        =    0.0000
    Residual |   408107984        70  5830114.06   R-squared       =    0.3574
-------------+----------------------------------   Adj R-squared   =    0.3298
       Total |   635065396        73  8699525.97   Root MSE        =    2414.6

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|                     Beta
-------------+----------------------------------------------------------------
         mpg |  -86.78928   83.94335    -1.03   0.305                -.1702392
      weight |   4.364798   1.167455     3.74   0.000                 1.150126
      length |  -104.8682   39.72154    -2.64   0.010                 -.791671
       _cons |   14542.43   5890.632     2.47   0.016                        .
------------------------------------------------------------------------------

[Andrew Musau]

ssc install esttab

sysuse auto, clear
regress price mpg weight length, beta
qui esttab, beta
mat l e(beta)

Res.:

. sysuse auto, clear
(1978 Automobile Data)

. regress price mpg weight length, beta

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(3, 70)        =     12.98
       Model |   226957412         3  75652470.6   Prob > F        =    0.0000
    Residual |   408107984        70  5830114.06   R-squared       =    0.3574
-------------+----------------------------------   Adj R-squared   =    0.3298
       Total |   635065396        73  8699525.97   Root MSE        =    2414.6

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|                     Beta
-------------+----------------------------------------------------------------
         mpg |  -86.78928   83.94335    -1.03   0.305                -.1702392
      weight |   4.364798   1.167455     3.74   0.000                 1.150126
      length |  -104.8682   39.72154    -2.64   0.010                 -.791671
       _cons |   14542.43   5890.632     2.47   0.016                        .
------------------------------------------------------------------------------

. qui esttab, beta

. mat l e(beta)

e(beta)[1,4]
           mpg      weight      length       _cons
y1  -.17023915   1.1501263    -.791671          .z

[Victor Smith]

Problem solved, thanks Andrew!