Coding Help on Stata

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I have an unbalanced panel data set that gives me information on how much banks lend in different areas. Geography id and bank id are numeric variables that were created using a Stata command like egen id=group(var).

The geography id goes from 1 to n and the bank id goes from 1 to k. To give you a more concrete idea of how my data look: 

Geography ID (gid) | Bank ID (bid) | lending
-----------------------------------------------
1                  | 1             | 25
1                  | 2             | 32
1                  | 4             | 83
----------------------------------------------
2                  | 1             | 76
2                  | 3             | 22
---------------------------------------------
3                  | 2             | 42
3                  | 3             | 12
3                  | 5             | 22
--------------------------------------------

My final goal is to create a dataframe that has all the pairwise combinations of the geographical areas such that:

    1     2      3 ......... n
-------------------------------
1|(1,1)  (1,2)  (1,3)......(1,n)
2|(2,1)  (2,2)  (2,3)......(2,n)
.|  .      .                 .
n|(n,1)    .         ......(n,n)

Such that entry (i,j) gives me:

(i,j)=(Lending from Banks Operating in Area i and j)/(Total Lending in Area i and j)

So for instance given the above data 

(1,1)=1       (1,2)=(25+76)/(25+32+83+76+22)     (1,3)=(32+42)/(25+32+83+42+12+22)

I have a feeling that as a first step I should use levelsof and bysort in a loop but I am unsure on how exactly to tackle the problem.

Even if you can't provide an exact solution I would be extremely grateful receiving any help or suggestion. Although I prefer Stata I also have some knowledge of Matlab/R, so if you think it would be more suitable for that problem I am open to suggestions.

stata r matlab
1 Answers

Here's an R method:

x <- data.frame(
  geoid   = c(1,1,1, 2,2, 3,3,3),
  bankid  = c(1,2,4, 1,3, 2,3,5),
  lending = c(25,32,83, 76,22, 42,12,22)
)

myfunc <- function(x, i, j) {
  geos <- x$geoid %in% c(i, j)
  banks <- with(x, intersect(bankid[geoid == i], bankid[geoid == j]))
  with(x, sum(lending[geos & bankid %in% banks]) / sum(lending[geos]))
}

outer(unique(x$geoid), unique(x$geoid),
      function(i,j) mapply(myfunc, list(x), i, j))
#           [,1]      [,2]      [,3]
# [1,] 1.0000000 0.4243697 0.3425926
# [2,] 0.4243697 1.0000000 0.1954023
# [3,] 0.3425926 0.1954023 1.0000000

It's not the most efficient, but it's a start. It's difficult (I think) to do this truly vectorized, since each subset requires intersections, though I'm sure this could be optimized to not require re-calculating intersect(bankid...) twice for each equivalent pair (if that's a performance factor).

Edit: slightly more efficient process that does not re-calculate equivalent pairs of geoid:

Split the data by geo:

geox <- split(x, x$geoid)

myfunc <- function(i, j) {
  if (i >= j) return(NA)
  banks <- intersect(geox[[i]]$bankid, geox[[j]]$bankid)
  sum(with(geox[[i]], lending[ bankid %in% banks ]),
      with(geox[[j]], lending[ bankid %in% banks ])) /
    sum(geox[[i]]$lending, geox[[j]]$lending)
}

o <- outer(seq_along(geox), seq_along(geox),
           function(i,j) mapply(myfunc, i, j))
o
#      [,1]      [,2]      [,3]
# [1,]   NA 0.4243697 0.3425926
# [2,]   NA        NA 0.1954023
# [3,]   NA        NA        NA

(Just to prove we only calculated the minimum set.) Now, flip the upper triangle's data to lower triangle:

o[which(lower.tri(o),TRUE)] <- o[which(upper.tri(o),TRUE)]
o
#           [,1]      [,2]      [,3]
# [1,]        NA 0.4243697 0.3425926
# [2,] 0.4243697        NA 0.1954023
# [3,] 0.3425926 0.1954023        NA

And assign the known-value of 1 to the diagonal:

diag(o) <- 1
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