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A scalable way of doing this without the need to save individual
coefficients uses Mata (and Ben Jann's -moremata- package, SSC).

sysuse auto, clear
constraint 1 price = weight
constraint 2 displacement = weight
constraint 3 gear_ratio = -foreign
constraint 4 _cons = 0

forv i=1/4 {
	cnsreg mpg price weight displacement gear_ratio foreign length, c(`i')
	mat mBeta = (nullmat(mBeta) , e(b)')
mat list mBeta

// max and min
mata: st_matrix("vBetaMax", rowmax(st_matrix("mBeta")))
mata: st_matrix("vBetaMin", rowmin(st_matrix("mBeta")))
mat list vBetaMax
mat list vBetaMin

// median (needs Ben Jann's -moremata-, SSC)
mata mata mlib index
mata: st_matrix("vBetaMed", mm_quantile(st_matrix("mBeta")', 1, .5 )')
mat list vBetaMed


On Tue, Aug 9, 2011 at 2:52 PM, Zhi Su <> wrote:
> Dear Statalists,
>  I wan to get some statistics of coefficients of the same independent
> in regressions subjected to different constraints.
>  For example, I run the following regression four times. Each time
> the regression is subjected to a constraint. Here is the code for
> regression in constraint 1.
>  cnsreg y x1 x2 x3 x4,constraints(1)
>  scalar b11=_b[x1]
>  scalar b12=_b[x2]
>  scalar b13=_b[x3]
>  scalar b14=_b[x4]
>  then I save the coefficients for x1, x2, x3, x4 in scalars
>  Then I get a group of scalars:
>  b11 b12 b13 b14 for the regression with constraint1
>  b21 b22 b23 b24 for the regression with constraint2
>  b31 b32 b33 b34 for the regression with constraint3
>  b41 b42 b43 b44 for the regression with constraint4
>  Now b11 b21 b31 b41 are all coefficients for x1. I want to determine
> which one among them is maximum,minimum, mode and median of this group
> of scalars.
>  What if the number of constraints is over 100, how can I pick the
> maximum,minimum, mode and median among a group of scalars of interest?
>  Thank you!

Zhi Su
348 Holmes Hall
Northeastern University
360 Huntington Avenue
Boston, MA 02115
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