Hi Kit,
to get the lower triangle -help mf_lowertriangle-
mata:
void function runsum(string scalar vname)
{
real matrix L
real matrix runsum
real scalar resindex
real scalar vnew
vnew = vname+"_sum"
st_view(A,.,vname)
L=lowertriangle(J(rows(A),rows(A),1))
runsum = L*A
resindex = st_addvar("double",vnew)
st_store((1,rows(runsum)),resindex,runsum)
}
end
Nicola
Kit Baum wrote:
Here's a Mata solution (no doubt hideously inefficient, but I can't
find all the fns. I'm looking for) that appears to be just as precise
as a -generate, sum- at doing a running sum. It could readily operate
on a column vector instead of a Stata variable if that is desired.
Note that a running sum can be calculated by premultiplying by a square
matrix with 1's in the lower triangle, 0's above...
clear
set obs 1000
g double x = (uniform()-0.5)*10^(int(_n/10))
g sort = uniform()
sort sort
drop sort
g double sum = sum(x)
mata:
void function runsum(string scalar vname)
{
real matrix L
real matrix runsum
real scalar resindex
real scalar vnew
vnew = vname+"_sum"
st_view(A,.,vname)
// if(cols(A)>1) A=A'
L = J(rows(A),rows(A),0)
for (i=1; i<=rows(A); i++) {
for(j=1; j<=i; j++) {
L[i,j] = 1
}
}
runsum = L*A
resindex = st_addvar("double",vnew)
st_store((1,rows(runsum)),resindex,runsum)
}
end
mata runsum("x")
g double discrep = sum - x_sum
su
l, sep(0)
Kit Baum, Boston College Economics
http://ideas.repec.org/e/pba1.html
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