Suppose you have a model in which you want to adjust some of the coefficients, in order to display in a table alongside the original coefficients.
As an example, consider a simple regression model. We will adjust the model by recentering the independent variable. In a simple model, the slope remains unchanged and we just alter the constant.
Set up:
. sysuse auto
(1978 Automobile Data)
. summarize mpg
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
mpg | 74 21.2973 5.785503 12 41
. scalar mu = r(mean)
Run the model, store the results for later display.
. regress price mpg
Source | SS df MS Number of obs = 74
-------------+---------------------------------- F(1, 72) = 20.26
Model | 139449474 1 139449474 Prob > F = 0.0000
Residual | 495615923 72 6883554.48 R-squared = 0.2196
-------------+---------------------------------- Adj R-squared = 0.2087
Total | 635065396 73 8699525.97 Root MSE = 2623.7
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -238.8943 53.07669 -4.50 0.000 -344.7008 -133.0879
_cons | 11253.06 1170.813 9.61 0.000 8919.088 13587.03
------------------------------------------------------------------------------
. estimates store base
Next, let me alter the coefficient matrix "by hand". This is just to make it clear what alteration I am actually making. In this simple example, the constant will shift to the mean of price.
. matrix A = e(b)
. matrix A[rownumb(A,"y1"), colnumb(A,"_cons")] = _b[_cons] + _b[mpg]*mu
. *matrix A[1, 2] = _b[_cons] + _b[mpg]*mu // alternate form
. matrix list A
A[1,2]
mpg _cons
y1 -238.89435 6165.2568
Now we define a program that makes this alteration (it is only useable with this one regression!).
. capture program drop recenter
. program define recenter, eclass
1. version 12
2. syntax , matrix(name) mu(real)
3.
. matrix `matrix'[rownumb(`matrix',"y1"), colnumb(`matrix',"_cons")] = _b[_co
> ns] + _b[mpg]*mu
4.
. ereturn repost b = `matrix'
5. end
The key elements of the above program are the ugly expression that replaces the constant, and the ereturn repost b, which gives us back the altered e(b) as well as the rest of the ereturn results from the previous regression. (Please note that the e(V) is now actually wrong!)
Calculate the altered coefficients, using the program. (Note we convert our scalar mu, into a macro.)
. recenter , matrix(A) mu(`=mu')
And finally, table the results:
. estimates table base .
----------------------------------------
Variable | base active
-------------+--------------------------
mpg | -238.89435 -238.89435
_cons | 11253.061 6165.2568
----------------------------------------