sambal.gms : Social Accounting Matrix Balancing Problem

Description

A Social Accounting Matrix captures all the circular flows in an
economy and is called balanced if the row total equal the column
totals. A sample problem illustrates the use of Nonlinear Programming
to balance such matrices.


Reference

  • Zenios, S A, Drud, A S, and Mulvey, J, Balancing some large Social Accounting Matrices with Nonlinear Programming. Tech. rep., Department of Civil Engineering, Princeton University, 1986.

Small Model of Type : NLP


Category : GAMS Model library


Main file : sambal.gms

$title Social Accounting Matrix Balancing Problem (SAMBAL,SEQ=77)

$onText
A Social Accounting Matrix captures all the circular flows in an
economy and is called balanced if the row total equal the column
totals. A sample problem illustrates the use of Nonlinear Programming
to balance such matrices.


Zenios, S A, Drud, A S, and Mulvey, J, Balancing some large Social
Accounting Matrices with Nonlinear Programming. Tech. rep.,
Department of Civil Engineering, Princeton University, 1986.

Keywords: nonlinear programming, social accounting matrix
$offText

Set i 'accounts' / lab, h1, h2, p1, p2 /;

Alias (i,j);

Table xb(i,j) 'original estimate'
               lab   h1   h2   p1   p2
   lab               15    3  130   80
   h1           na
   h2           na
   p1                15  130        20
   p2                25   40   55     ;

Parameter
   tb(i)   'original totals'   / lab 220, (h1,h2) na, p1 190, p2 105 /
   tw(i)   'wights for totals'
   xw(i,j) 'weights for cells';

tw(i)   = 1;
xw(i,j) = 1$xb(i,j);
tw(i)$(mapval(tb(i))     = mapval(na)) = 0;
xw(i,j)$(mapval(xb(i,j)) = mapval(na)) = 0;

display tw, xw;

Variable
   x(i,j)  'estimated cells'
   t(i)    'estimated totals'
   dev     'deviations';

Equation
   rbal(i) 'row balance'
   cbal(j) 'column balance'
   devsqr  'definition of square deviations';

rbal(i).. t(i) =e= sum(j$xb(i,j), x(i,j));

cbal(j).. t(j) =e= sum(i$xb(i,j), x(i,j));

devsqr..  dev  =e=   sum((i,j)$xw(i,j), xw(i,j)*sqr(xb(i,j) - x(i,j))/xb(i,j))
                   + sum(i$tw(i), tw(i)*sqr(tb(i) - t(i))/tb(i));

Model bal / all /;

x.l(i,j) = xb(i,j)$xw(i,j);
t.l(i)   = tb(i)$tw(i);

solve bal using nlp minimizing dev;