how to do calculations with the coefficients that i obtained

[Olivia Johns]

after running OLS regression,

I want to automatically do calculations with the coefficients I obtained.

if for example I want to get a number by doing

value = (b1 + b2)/1 - (a1 +a2)

how can I code this?

Thanks

[Charlie Joyez]

Olivia,
First, please detail what you mean by b1, b2, a1 and a2.
Generally b stands for the coefficients, and I assume a stand for the value of the variable, but this is not obvious.

If I'm right on a and b, you should use some locals from the matrix of coefficients here.

 matrix b = e(b)
local b1=b[1,1]
local b2=b[1,2]
gen newvar=(`b1'+`b2')/1-(a1+a2)

Best,
Charlie

[Ariel Karlinsky]

you can use the expression builder (data-other utilities - hand calculator - create - coefficients).
you can also see the names of the coefficients by using the coeflegend option:

. sysuse auto
(1978 Automobile Data)

. reg price mpg weight

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(2, 71)        =     14.74
       Model |   186321280         2  93160639.9   Prob > F        =    0.0000
    Residual |   448744116        71  6320339.67   R-squared       =    0.2934
-------------+----------------------------------   Adj R-squared   =    0.2735
       Total |   635065396        73  8699525.97   Root MSE        =      2514

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |  -49.51222   86.15604    -0.57   0.567    -221.3025     122.278
      weight |   1.746559   .6413538     2.72   0.008      .467736    3.025382
       _cons |   1946.069    3597.05     0.54   0.590    -5226.245    9118.382
------------------------------------------------------------------------------

. reg price mpg weight, coeflegend

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(2, 71)        =     14.74
       Model |   186321280         2  93160639.9   Prob > F        =    0.0000
    Residual |   448744116        71  6320339.67   R-squared       =    0.2934
-------------+----------------------------------   Adj R-squared   =    0.2735
       Total |   635065396        73  8699525.97   Root MSE        =      2514

------------------------------------------------------------------------------
       price |      Coef.  Legend
-------------+----------------------------------------------------------------
         mpg |  -49.51222  _b[mpg]
      weight |   1.746559  _b[weight]
       _cons |   1946.069  _b[_cons]
------------------------------------------------------------------------------

. disp (_b[mpg]+_b[weight])/(1-_b[_cons])
.02455731

[Sebastian Kripfganz]

A better way to compute nonlinear combinations of coefficients is to use the nlcom postestimation command as it also provides corresponding standard errors.

Based on Ariel's example:

. nlcom (_b[mpg]+_b[weight])/(1-_b[_cons])

       _nl_1:  (_b[mpg]+_b[weight])/(1-_b[_cons])

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .0245573   .0148019     1.66   0.097    -.0044539    .0535685
------------------------------------------------------------------------------