Prediction out of sample using a data frame of coefficients

[Daniela Arlia]

Hi everyone,

I have run an OLS regression on a different software than STATA and stored estimated coefficients. I want to use these estimated coefficients to make predictions in STATA. How can I do that?

Thanks.

[Andrew Musau]

The easiest way is to estimate the model again in Stata. The OLS command is regress. In any case, predictions are straightforward in linear regression models.

sysuse auto, clear
regress mpg price weight
predict mpghat1, xb
mat l e(b)
gen mpghat2= (-.00009351*price) + (-.00581754*weight) + 39.439656
format %9.3f mpghat1 mpghat2
l mpghat1 mpghat2 in 1/10, sep(0)

Res.:

. regress mpg price weight

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(2, 71)        =     66.85
       Model |  1595.93249         2  797.966246   Prob > F        =    0.0000
    Residual |  847.526967        71  11.9369995   R-squared       =    0.6531
-------------+----------------------------------   Adj R-squared   =    0.6434
       Total |  2443.45946        73  33.4720474   Root MSE        =     3.455

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       price |  -.0000935   .0001627    -0.57   0.567     -.000418    .0002309
      weight |  -.0058175   .0006175    -9.42   0.000    -.0070489   -.0045862
       _cons |   39.43966   1.621563    24.32   0.000     36.20635    42.67296
------------------------------------------------------------------------------

. 
. predict mpghat1, xb

. 
. mat l e(b)

e(b)[1,3]
         price      weight       _cons
y1  -.00009351  -.00581754   39.439656

. 
. gen mpghat2= (-.00009351*price) + (-.00581754*weight) + 39.439656

. 
. format %9.3f mpghat1 mpghat2

. 
. l mpghat1 mpghat2 in 1/10, sep(0)

     +-------------------+
     | mpghat1   mpghat2 |
     |-------------------|
  1. |  22.011    22.011 |
  2. |  19.507    19.507 |
  3. |  23.726    23.726 |
  4. |  20.082    20.082 |
  5. |  14.972    14.972 |
  6. |  17.548    17.548 |
  7. |  26.050    26.050 |
  8. |  19.873    19.873 |
  9. |  15.898    15.898 |
 10. |  19.278    19.278 |
     +-------------------+