Description
CESAM illustrates a cross entropy technique for estimating the cells of a consistent SAM assuming that the initial data are inconsistent and measured with error. The method is applied to a stylized macro SAM for Mozambique. Some macro control totals are assumed known with error, and also all the row and column totals are assumed known only with error. We assume that the user can specify a prior estimate of the standard error of the estimates of the row and column sums and of the macro control totals.
Small Model of Types : MCP nlp
Category : GAMS Model library
Main file : cesam.gms
$title Cross Entropy SAM Estimation (CESAM,SEQ=228)
$onText
CESAM illustrates a cross entropy technique for estimating the cells
of a consistent SAM assuming that the initial data are inconsistent
and measured with error. The method is applied to a stylized macro
SAM for Mozambique. Some macro control totals are assumed known with
error, and also all the row and column totals are assumed
known only with error. We assume that the user can specify
a prior estimate of the standard error of the estimates of the row
and column sums and of the macro control totals.
Robinson, S, Cattaneo, A, and El-Said, M, Updating and Estimating
a Social Accounting Matrix Using Cross Enthropy Methods. Economic
System Research 13, 1 (2001).
Golan, G, Judge, G, and Miller, D, Maximum Enthropy Econometrics.
John Wiley and Sons, 1996.
Programmed by Sherman Robinson and Moataz El-Said, November 2000.
Trade and Macroeconomics Division
International Food Policy Research Institute (IFPRI)
2033 K Street, N.W.
Washington, DC 20006 USA
Email: S.Robinson@CGIAR.ORG
M.El-Said@CGIAR.ORG
Data set is based on a SAM developed by C. Arndt, A. S. Cruz, H. T.
Jensen, S. Robinson, and F. Tarp, "Social Accounting Matrices
for Mozambique - 1994 and 1995." TMD Discussion Paper No. 28, IFPRI,
July 1998.
Original version programmed by Sherman Robinson and Andrea Cattaneo.
Keywords: mixed complementarity problem, micro economics, cross entropy,
social accounting matrix
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Set
i 'sam accounts'
/ ACT 'Activities', COM 'Commodities', FAC 'Factors'
ENT 'Enterprises', HOU 'Households', GRE 'Govt recurrent expenditures'
GIN 'Govt investment', CAP 'Capital account', ROW 'Rest of world'
TOTAL /
ii(i) 'all accounts in i except total'
/ ACT 'Activities', COM 'Commodities', FAC 'Factors'
ENT 'Enterprises', HOU 'Households', GRE 'Govt recurrent expenditures'
GIN 'Govt investment', CAP 'Capital account', ROW 'Rest of world' /
macro 'macro controls' / gdpfc2, gdp2 /
* The set jwt defines the dimension of the support set for the error
* distribution and the number of weights that must be estimated for each
* error. In this case, we specify a five parameter error distribution.
* For a three parameter distribution, jwt is set to / 1*3 /.
jwt 'set of weights for errors in variables' / 1*5 /;
* ii(i) = yes;
* ii("Total") = no;
Alias (i,j), (ii,jj);
* SAM DATABASE
Table SAM(i,j) 'social accounting matrix'
ACT COM FAC ENT
ACT 0.0 14827.4240 0.0 0.0
COM 7917.5040 0.0 0.0 0.0
FAC 9805.4140 0.0 0.0 0.0
ENT 0.0 0.0 3699.7060 0.0
HOU 0.0 0.0 6031.3080 3417.5060
GRE 733.6000 357.4000 74.4000 165.2000
GIN 0.0 0.0 0.0 0.0
CAP 0.0 0.0 0.0 150.0000
ROW 0.0 5573.8150 0.0 0.0
Total 18456.5180 20758.639 9805.414 3732.706
+ HOU GRE GIN CAP
ACT 2101.0490 -0.3270 0.0 0.0
* COM 6753.3320 1764.5000 2118.5000 2197.7980
COM 6953.3320 1564.5000 2518.5000 2597.7980
FAC 0.0 0.0 0.0 0.0
ENT 0.0 33.0000 0.0 0.0
HOU 0.0 29.6000 0.0 0.0
GRE 139.5000 0.0 0.0 0.0
GIN 0.0 0.0 0.0 0.0
CAP 649.1560 -356.6730 -406.2000 0.0
ROW 0.0 0.0 0.0 0.0
Total 9643.037 1470.1 1712.3 2197.798
+ ROW Total
ACT 1488.1570 18416.303
COM 0.0 20751.634
FAC 0.0 9805.414
ENT 0.0 3732.706
HOU 209.5010 9687.915
GRE 0.0 1470.1
GIN 1712.3000 1712.3
CAP 2163.8570 2200.14
ROW 0.0 5573.815
Total 5573.815 ;
* Parameters and Scalars
Parameter
SAM0(i,j) 'base SAM transactions matrix'
T0(i,j) 'matrix of SAM transactions (flow matrix)'
T1(i,j) 'SAM transactions adjusted to eliminate negative entries'
Abar0(i,j) 'prior SAM coefficient matrix'
Abar1(i,j) 'prior SAM adjusted to eliminate negative coefficients'
Target0(i) 'targets for macro SAM column totals'
vbar1(i,jwt) 'error support set 1'
vbar2(macro,jwt) 'error support set 2'
wbar1(i,jwt) 'weights on error support set 1'
wbar2(macro,jwt) 'weights on error support set 2'
sigmay1(i) 'prior standard error of column sums'
sigmay2(macro) 'prior standard error of macro aggregates'
epsilon 'tolerance to allow zero entries in SAM';
Scalar
gdp0 'base GDP'
gdp00 'GDP from final SAM'
gdpfc0 'GDP at factor cost';
* Initializing Parameters
SAM("TOTAL",jj) = sum(ii, SAM(ii,jj));
SAM(ii,"TOTAL") = sum(jj, SAM(ii,jj));
sam0(i,j) = sam(i,j);
* Divide SAM entries by 1000 for better scaling.
* The SAM is scaled to enhance solver efficiency. Nonlinear solvers are
* more efficient if variables are scaled similarly. In this case,
* coefficients to be estimated range between 0 and 1, so SAM values
* are also scaled.
Scalar scalesam 'scaling value' / 1000 /;
sam(i,j) = sam(i,j)/scalesam ;
Abar0(ii,jj)$SAM(ii,jj) = SAM(ii,jj)/SAM("TOTAL",jj);
T0(ii,jj) = SAM(ii,jj);
T0("TOTAL",jj) = sum(ii, SAM(ii,jj));
T0(ii,"TOTAL") = sum(jj, SAM(ii,jj));
epsilon = .00001;
display T0, Abar0;
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CROSS ENTROPY
RED ALERT!!!
The ENTROPY DIFFERENCE procedure uses LOGARITHMS: negative flows in
the SAM are NOT GOOD!!!
The option used here is to detect any negative flows and net them out
of their respective symmetric cells, e.g.
negative flow column to row is set to zero
and added to corresponding row to column as a positive number.
The entropy difference method can then be implemented.
After balancing, the negative SAM values are returned to their
original cells for printing.
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Set red(i,j) 'set of negative SAM flows';
Parameter
redsam(i,j) 'negative SAM values only'
rtot(i) 'row total'
ctot(i) 'column total';
rtot(ii) = sum(jj, T0(ii,jj));
ctot(jj) = sum(ii, T0(ii,jj));
red(ii,jj)$(T0(ii,jj) < 0) = yes;
redsam(ii,jj) = 0;
redsam(ii,jj)$red(ii,jj) = T0(ii,jj);
redsam(jj,ii)$red(ii,jj) = T0(ii,jj);
*Note that redsam includes each entry twice, in corresponding row
*and column. So, redsam need only be subtracted from T0.
T1(ii,jj) = T0(ii,jj) - redsam(ii,jj);
T1("Total",jj) = sum(ii, T1(ii,jj));
T1(ii,"Total") = sum(jj, T1(ii,jj));
redsam("total",jj) = sum(ii, redsam(ii,jj));
redsam(ii,"total") = sum(jj, redsam(ii,jj));
sam(ii,"total") = sum(jj, T1(ii,jj));
sam("total",jj) = sum(ii, T1(ii,jj));
rtot(ii) = sum(jj, T1(ii,jj));
ctot(jj) = sum(ii, T1(ii,jj));
Abar1(ii,jj) = T1(ii,jj)/sam("total",jj);
display "NON-NEGATIVE SAM", redsam, T1, Abar0, Abar1, rtot, ctot;
* Define set of elements of SAM that can be nonzero. In this case, only
* elements which are nonzero in initial SAM.
Set NONZERO(i,j) 'SAM elements that can be nonzero';
NONZERO(ii,jj)$(Abar1(ii,jj)) = yes;
* Initializing Parameters after accounting for negative values
* Note that target column sums are being set to average of initial
* row and column sums. Initial column sums or other values
* could have been used instead, depending on knowledge of data quality
* and any other prior information.
target0(ii) = (sam(ii,"total") + sam("total",ii))/2;
gdpfc0 = T1("fac","act");
gdp0 = T1("fac","act") + T1("gre","act") - T1("act","gre") + T1("gre","com");
display gdpfc0, gdp0;
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Define variable bounds on errors
Start from assumed prior knowledge of the standard error (perhaps due
to measurement error) of the column sums. Below, we assume that all
column sums have a standard error of 5%. This is a Bayesian prior,
not a maintained hypothesis.
The estimated error is weighted sum of elements in an error support
set:
ERR(ii) = sum(jwt, W(ii,jwt)*VBAR(ii,jwt))
where the W's are estimated in the CE procedure.
The prior variance of these errors is given by:
(sigmay(ii))**2 = sum(jwt, WBAR(ii,jwt)*(VBAR(ii,jwt))**2)
where the WBAR's are the prior on the weights.
The VBARs are chosen to define a domain for the support set of +/- 3
standard errors. The prior on the weights, WBAR, are then calculated
to yield the specified prior on the standard error, sigmay.
In Robinson, Cattaneo, and El-Said (2001), we specify prior weights
(WBAR) that are uniform and set the prior standard error by the
choice of support set, VBAR. In that paper, we use a three-weight
specification (jwt / 1*3 /);
We define two sets of errors with separate weights, W1 and W2. The
first is for specifying errors on column sums, the second for errors
on macro aggregates (defined in the set macro).
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* First, define standard error for errors on column sums.
sigmay1(ii) = 0.05*target0(ii);
* This code assumes a prior mean of zero and a two-parameter
* distribution with specified prior standard error. There are three
* weights, W(ii,jwt), to be estimated. The actual moments are estimated
* as part of the estimation procedure.
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* Set constants for 3-weight error distribution
vbar1(ii,"1") = -3*sigmay1(ii);
vbar1(ii,"2") = 0;
vbar1(ii,"3") = -3*sigmay1(ii);
wbar1(ii,"1") = 1/18;
wbar1(ii,"2") = 16/18;
wbar1(ii,"3") = 1/18;
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This code assumes a prior mean of zero and a prior value of kurtosis
consistent with a prior normal distribution with mean zero, variance
sigmay**2, and kurtosis equal to 3*sigmay**4. The addition of a prior
on kurtosis requires estimation of 5 weights (jwt = 5);
The prior weights wbar are specified so that:
sum(jwt, wbar(ii,jwt)*vbar(ii,jwt)**4) = 3*sigmay(ii,jwt)**4
as well as defining the variance as above.
The prior weights and support set are also symmetric, so the prior
on all odd moments is zero. The choice of +/- 1 standard error
for vbar(ii,"2") and vbar(ii,"4") is arbitrary.
The actual moments are estimated as part of the estimation procedure.
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* Set constants for 5-weight error distribution
vbar1(ii,"1") = -3*sigmay1(ii);
vbar1(ii,"2") = -1*sigmay1(ii);
vbar1(ii,"3") = 0;
vbar1(ii,"4") = 1*sigmay1(ii);
vbar1(ii,"5") = 3*sigmay1(ii);
wbar1(ii,"1") = 1/72;
wbar1(ii,"2") = 27/72;
wbar1(ii,"3") = 16/72;
wbar1(ii,"4") = 27/72;
wbar1(ii,"5") = 1/72;
* Second, define standard errors for errors on macro aggregates
sigmay2("gdpfc2") = 0.05*gdpfc0;
sigmay2("gdp2") = 0.05*gdp0;
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* Set constants for 3-weight error distribution
vbar2(ii,"1") = -3*sigmay2(ii);
vbar2(ii,"2") = 0;
vbar2(ii,"3") = -3*sigmay2(ii);
wbar2(ii,"1") = 1/18;
wbar2(ii,"2") = 16/18;
wbar2(ii,"3") = 1/18;
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* Set constants for 5-weight error distribution
vbar2(macro,"1") = -3*sigmay2(macro);
vbar2(macro,"2") = -1*sigmay2(macro);
vbar2(macro,"3") = 0;
vbar2(macro,"4") = 1*sigmay2(macro);
vbar2(macro,"5") = 3*sigmay2(macro);
wbar2(macro,"1") = 1/72;
wbar2(macro,"2") = 27/72;
wbar2(macro,"3") = 16/72;
wbar2(macro,"4") = 27/72;
wbar2(macro,"5") = 1/72;
display vbar1, vbar2, sigmay1, sigmay2;
Variable
A(ii,jj) 'post SAM coefficient matrix'
TSAM(ii,jj) 'post matrix of SAM transactions'
Y(ii) 'row sum of SAM'
X(ii) 'column sum of SAM'
ERR1(ii) 'error value on column sums'
ERR2(macro) 'error value for macro aggregates'
W1(ii,jwt) 'error weights'
W2(macro,jwt) 'error weights'
DENTROPY 'entropy difference (objective)'
GDPFC 'GDP at factor cost'
GDP 'GDP at market prices';
* INITIALIZE VARIABLES
A.l(ii,jj) = Abar1(ii,jj);
TSAM.l(ii,jj) = T1(ii,jj);
Y.l(ii) = target0(ii);
X.l(ii) = target0(ii);
ERR1.l(ii) = 0.0;
ERR2.l(macro) = 0.0;
W1.l(ii,jwt) = wbar1(ii,jwt);
W2.l(macro,jwt) = wbar2(macro,jwt);
DENTROPY.l = 0;
GDPFC.l = gdpfc0;
GDP.l = gdp0;
* CORE EQUATIONS
Equation
SAMEQ(i) 'row and column sum constraint'
SAMMAKE(i,j) 'make SAM flows'
ERROR1EQ(i) 'definition of error term 1'
ERROR2EQ(macro) 'definition of error term 2'
SUMW1(i) 'sum of weights 1'
SUMW2(macro) 'sum of weights 2'
ENTROPY 'entropy difference definition'
ROWSUM(i) 'row target'
COLSUM(j) 'column target'
GDPFCDEF 'define GDP at factor cost'
GDPDEF 'define GDP';
*CORE EQUATIONS
SAMEQ(ii)..
Y(ii) =e= X(ii) + ERR1(ii);
SAMMAKE(ii,jj)$nonzero(ii,jj)..
TSAM(ii,jj) =e= A(ii,jj)*(X(jj) + ERR1(jj));
ERROR1EQ(ii)..
ERR1(ii) =e= sum(jwt, W1(ii,jwt)*vbar1(ii,jwt));
SUMW1(ii)..
sum(jwt, W1(ii,jwt)) =e= 1;
ENTROPY..
DENTROPY =e= sum((ii,jj)$nonzero(ii,jj), A(ii,jj)*(log(A(ii,jj) + epsilon)
- log(Abar1(ii,jj) + epsilon)))
+ sum((ii,jwt), W1(ii,jwt)*(log(W1(ii,jwt) + epsilon)
- log(wbar1(ii,jwt) + epsilon)))
+ sum((macro,jwt), W2(macro,jwt)*(log(W2(macro,jwt) + epsilon)
- log(wbar2(macro,jwt) + epsilon)));
* Note that we exclude one rowsum equation since if all but one column
* and rowsum are equal, the last one must also be equal. Walras' Law
* at work.
ROWSUM(ii)$(not sameas(ii,"ROW"))..
sum(jj, TSAM(ii,jj)) =e= Y(ii);
COLSUM(jj)..
sum(ii, TSAM(ii,jj)) =e= (X(jj) + ERR1(jj));
*ADDITIONAL MACRO CONTROL-TOTAL EQUATIONS
GDPFCDEF..
GDPFC =e= TSAM("fac","act") + ERR2("gdpfc2");
GDPDEF..
GDP =e= TSAM("fac","act") + TSAM("gre","act")
- TSAM("act","gre") + TSAM("gre","com") + ERR2("gdp2");
ERROR2EQ(macro)..
ERR2(macro) =e= sum(jwt, W2(macro,jwt)*vbar2(macro,jwt));
SUMW2(macro)..
sum(jwt, W2(macro,jwt)) =e= 1;
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Define bounds for cell values
Defining equation SAMMAKE over non-zero elements of A ($Abar1(ii,jj))
guarantees that the zero structure of the original SAM is maintained
in the estimated SAM. Fixing all the zero entries to zero greatly
reduces the size of the estimation problem. If it is desired to
allow a zero entry to become nonzero in the estimated SAM, then
the condition $ABAR1(ii,jj) must be replaced with a new set that
does not include cells which are currently zero but may be nonzero.
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A.lo(ii,jj)$nonzero(ii,jj) = 0;
A.up(ii,jj)$nonzero(ii,jj) = 1;
A.fx(ii,jj)$(not nonzero(ii,jj)) = 0;
TSAM.lo(ii,jj) = 0.0;
TSAM.up(ii,jj) = +inf;
TSAM.fx(ii,jj)$(not nonzero(ii,jj)) = 0;
* Upper and lower bounds on the error weights
W1.lo(ii,jwt) = 0;
W1.up(ii,jwt) = 1;
W2.lo(macro,jwt) = 0;
W2.up(macro,jwt) = 1;
* Set target column sums, X. If these are not fixed, then the column sum
* constraints will not be binding and the solution values or ERR1 will be 0.
X.fx(ii) = TARGET0(ii);
* Fix Macro aggregates.
* If these are not fixed, then the macro constraints will not be binding
* and the solution values of ERR2 will be zero.
GDP.fx = GDP0;
GDPFC.fx = GDPFC0;
Model SAMENTROP / all /;
option iterLim = 5000, limRow = 0, limCol = 0, solPrint = on;
* SAMENTROP.optFile = 1;
SAMENTROP.holdFixed = 1;
* option nlp = CONOPT;
* SAMENTROP.workSpace = 25.0;
solve SAMENTROP using nlp minimizing dentropy;
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(alternative formulation) MCP Formulation
Add code restating the nonlinear-programming (NLP) minimization
problem as an MCP problem solved using the MCP solver.
The code below is a translation of the NLP problem into a
mixed complementarity problem (MCP), which can be solved
using an MCP solver in GAMS. The translation was done using
a preliminary version of a program called NLP2MCP written
by Michael Ferris and Jeffrey Horn (1998) at the University
of Wisconsin. The translation adds "shadow price" or
complementarity variables for all constraint equations and
also provides equations for all the first-order conditions
for minimizing the objective function. The resulting model
is square with as many variables as equations.
$offText
Alias (macro,a_macro), (ii,a_ii), (jj,a_jj), (jwt,a_jwt);
* "SHADOW PRICE" OR COMPLEMENTARITY VARIABLES OR
* LAGRANGE MULTIPLIERS FOR ALL CONSTRAINT EQUATIONS##########
Variable
m_SAMEQ(i) 'multiplier for row and column sum constraint'
m_SAMMAKE(i,j) 'multiplier for make SAM flows constraint'
m_ERROR1EQ(i) 'multiplier for definition of error term 1'
m_ERROR2EQ(macro) 'multiplier for definition of error term 2'
m_SUMW1(i) 'multiplier for sum of weights 1 constraint'
m_SUMW2(macro) 'multiplier for sum of weights 1 constraint'
m_ROWSUM(i) 'multiplier for row target constraint'
m_COLSUM(j) 'multiplier for column target constraint'
m_GDPFCDEF 'multiplier for GDP at factor cost constraint'
m_GDPDEF 'multiplier for GDP at market pricesconstraint';
* EQUATIONS FOR THE FIRST ORDER CONDITIONS
* FOR MINIMIZING THE OBJECTIVE FUNCTION
Equation
d_A(a_ii,a_jj) 'FOC wrt the choice variable A'
d_TSAM(a_ii,a_jj) 'FOC wrt the variable TSAM'
d_Y(a_ii) 'FOC wrt the variable Y'
d_X(a_ii) 'FOC wrt the variable X'
d_ERR1(a_ii) 'FOC wrt the variable ERR1'
d_ERR2(a_macro) 'FOC wrt the variable ERR2'
d_W1(a_ii,a_jwt) 'FOC wrt the choice variable W1'
d_W2(a_macro,a_jwt) 'FOC wrt the choice variable W2'
d_GDPFC 'FOC wrt the macro control variable GDPFC'
d_GDP 'FOC wrt the macro control variable GDP';
* EQUATION: FOC wrt the choice variable A
d_A(a_ii,a_jj)..
((log(a(a_ii,a_jj) + epsilon) - log(abar1(a_ii,a_jj) + epsilon))
$(nonzero(a_ii,a_jj)) + a(a_ii,a_jj)*(1/(a(a_ii,a_jj) + epsilon))
$(nonzero(a_ii,a_jj)))
- sum((ii,jj)$(nonzero(ii,jj)), m_sammake(ii,jj)*(-(1$((sameas
(a_ii,ii) and sameas(a_jj,jj))))*(x(jj) + err1(jj))))
=e= 0;
* EQUATION: FOC wrt the variable TSAM
d_TSAM(a_ii,a_jj)..
- m_gdpfcdef*(-(1$((sameas(a_ii,"fac") and sameas(a_jj,"act")))))
- m_colsum(a_jj)
- m_gdpdef*(-(1$((sameas(a_ii,"fac") and sameas(a_jj,"act")))
+1$((sameas(a_ii,"gre") and sameas(a_jj,"act"))) - (1$((sameas
(a_ii,"act") and sameas(a_jj,"gre"))))+1$((sameas(a_ii,"gre")
and sameas(a_jj,"com")))))
- m_rowsum(a_ii)$((not sameas(a_ii,"ROW")))
- m_sammake(a_ii,a_jj)$(nonzero(a_ii,a_jj))
=e= 0;
* EQUATION: FOC wrt the variable Y
d_Y(a_ii)..
- m_sameq(a_ii)
- sum((ii)$((not sameas(ii,"ROW"))),m_rowsum(ii)*(-(1$((sameas(a_ii,ii))))))
=e= 0;
* EQUATION FOC wrt the variable X
d_X(a_ii)..
- sum(jj, m_colsum(jj)*(-(1$((sameas(a_ii,jj))))))
- sum(ii, m_sameq(ii) *(-(1$((sameas(a_ii,ii))))))
- sum((ii,jj)$(nonzero(ii,jj)), m_sammake(ii,jj)*(-a(ii,jj)*(1$((sameas(a_ii,jj))))))
=e= 0;
* EQUATION: FOC wrt the variable ERR1
d_ERR1(a_ii)..
- m_error1eq(a_ii) - sum(jj, m_colsum(jj)*(-(1$((sameas(a_ii,jj))))))
- sum(ii, m_sameq(ii)*(-(1$((sameas(a_ii,ii))))))
- sum((ii,jj)$(nonzero(ii,jj)), m_sammake(ii,jj)*(-a(ii,jj)*(1$((sameas(a_ii,jj))))))
=e= 0;
* EQUATION: FOC wrt the variable ERR2
d_ERR2(a_macro)..
- m_error2eq(a_macro) - m_gdpfcdef*(-(1$((sameas(a_macro,"gdpfc2")))))
- m_gdpdef*(-(1$((sameas(a_macro,"gdp2")))))
=e= 0;
* EQUATION: FOC wrt the choice variable W1
d_W1(a_ii,a_jwt)..
((log(w1(a_ii,a_jwt) + epsilon) - log(wbar1(a_ii,a_jwt) + epsilon))
+ w1(a_ii,a_jwt)*(1/(w1(a_ii,a_jwt) + epsilon)))
- sum(ii, m_error1eq(ii)*(-(vbar1(ii,a_jwt)$(sameas(a_ii,ii))))) - m_sumw1(a_ii)
=e= 0;
* EQUATION: FOC wrt the choice variable W2
d_W2(a_macro,a_jwt)..
((log(w2(a_macro,a_jwt) + epsilon) - log(wbar2(a_macro,a_jwt) + epsilon))
+ w2(a_macro,a_jwt)*(1/(w2(a_macro,a_jwt) + epsilon)))
- sum(macro, m_error2eq(macro)*(-(vbar2(macro,a_jwt)$(sameas(a_macro,macro)))))
- m_sumw2(a_macro)
=e= 0;
* EQUATION: FOC wrt the macro control variable GDPFC
d_GDPFC..
- m_gdpfcdef =e= 0;
* EQUATION: FOC wrt the macro control variable GDP
d_GDP..
- m_gdpdef =e= 0;
* DEFINE MODEL
* In GAMS the "." is used for pairing the complementarity variables
* and equations for the MCP solver. For example the equation
* defined by d_A is complementary to the variable A and must be
* defined over the same sets.
Model m_SAMENTROP / d_A.A, d_TSAM.TSAM
d_Y.Y
d_X.X
d_ERR1.ERR1
d_ERR2.ERR2
d_W1.W1
d_W2.W2
d_GDPFC.GDPFC
d_GDP.GDP
ERROR1EQ.m_ERROR1EQ
ERROR2EQ.m_ERROR2EQ
GDPFCDEF.m_GDPFCDEF
COLSUM.m_COLSUM
SAMEQ.m_SAMEQ
GDPDEF.m_GDPDEF
SUMW1.m_SUMW1
ROWSUM.m_ROWSUM
SUMW2.m_SUMW2
SAMMAKE.m_SAMMAKE /;
* SOLVE MODEL
* Shock the NLP solution
A.l(ii,jj) = 0.9*A.l(ii,jj);
solve m_SAMENTROP using mcp;
*Compare NLP and MCP results.
Scalar savedent;
savedent = dentropy.l;
DENTROPY.l = sum((ii,jj)$nonzero(ii,jj), A.l(ii,jj)*(log(A.l(ii,jj) + epsilon)
- log(Abar1(ii,jj) + epsilon)))
+ sum((ii,jwt), W1.l(ii,jwt)
* (log(W1.l(ii,jwt) + epsilon)
- log(wbar1(ii,jwt) + epsilon)))
+ sum((macro,jwt), W2.l(macro,jwt)
* (log(W2.l(macro,jwt) + epsilon)
- log(wbar2(macro,jwt) + epsilon)));
option decimals = 8;
display dentropy.l, savedent;
option decimals = 3;
* END MCP INCLUDE FILE
* NOTE ON THE USE OF "SAMEAS" GAMS COMMAND
* Undocumented Feature IN GAMS Manual
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Matching Set Elements
New features in GAMS allow one to introduce conditional statements
controlling execution in cases where certain items match up . The
syntax involves using the commands
sameas(setelement1,setelement2)
or
diag(setelement,setelement)
the SAMEAS command returns a true false indicator which is true
if the text string defining the name of set element 1 equals that
for setelement 2 and false otherwise. DIAG returns a 1 under
equality and a zero otherwise.
For example
x = sum((i,j)$(not sameas(i,j)), z(i)*z(j));
or
x = sum((i,j)$(diag(i,j) = 0), z(i)*z(j));
would exclude the cases where i = j from the sum
while
x = sum((i,j)$(sameas(i,"case1") or sameas(j,"case2")), z(i) + z(j));
would only include cases where the text for i equaled the string
"case1" or the text for j corresponded to "case2."
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* END Note on "sameas" GAMS command
* Parameters for reporting results
Parameter
Macsam1(i,j) 'assigned new balanced SAM flows from CE'
Macsam2(i,j) 'balanced SAM flows from entropy diff x scalesam'
SEM 'squared error measure'
percent1(i,j) 'percent change of new SAM from original SAM'
PosUnbal(i,j) 'positive unbalanced SAM'
PosBalan(i,j) 'positive balanced SAM'
Diffrnce(i,j) 'differnce btw original SAM and final SAM in values'
NormEntrop 'normalized entropy a measure of total uncertainty';
macsam1(ii,jj) = TSAM.l(ii,jj);
macsam1("total",jj) = sum(ii, macsam1(ii,jj));
macsam1(ii,"total") = sum(jj, macsam1(ii,jj));
macsam2(i,j) = macsam1(i,j)*scalesam;
SEM = sum((ii,jj), sqr(A.L(ii,jj) - Abar1(ii,jj)))/sqr(card(ii));
percent1(i,j)$(T1(i,j)) = 100*(macsam1(i,j) - T1(i,j))/T1(i,j);
PosUnbal(i,j) = T1(i,j) * scalesam;
PosBalan(i,j) = macsam2(i,j);
Diffrnce(i,j) = PosBalan(i,j) - PosUnbal(i,j);
NormEntrop = sum((ii,jj)$(Abar1(ii,jj)), A.l(ii,jj)*log(A.l(ii,jj)))
/ sum((ii,jj)$(Abar1(ii,jj)), Abar1(ii,jj)*log(Abar1(ii,jj)));
display macsam1, macsam2, percent1, sem, dentropy.l, PosUnbal, PosBalan, NormEntrop, Diffrnce;
* Return negative flows to initial cell position
macsam1(ii,jj) = macsam1(ii,jj) + redsam(ii,jj);
macsam1("total",jj) = sum(ii, macsam1(ii,jj));
macsam1(ii,"total") = sum(jj, macsam1(ii,jj));
macsam2(i,j) = macsam1(i,j)*scalesam;
gdp00 = macsam1("fac","act") + macsam1("gre","act")
- macsam1("act","gre") + macsam1("gre","com");
display macsam1, macsam2, gdp0, gdp00, gdp.l, gdpfc0, gdpfc.l;
Parameter ANEW(i,j);
* print some stuff
ANEW("total",jj) = sum(ii, A.l(ii,jj));
ANEW(ii,"total") = sum(jj, A.l(ii,jj));
ABAR1("total",jj) = sum(ii, ABAR1(ii,jj));
ABAR1(ii,"total") = sum(jj, ABAR1(ii,jj));
display ANEW, ABAR1;
Scalar meanerr1, meanerr2;
meanerr1 = sum(ii, abs(err1.l(ii)))/card(ii);
meanerr2 = sum(macro, abs(err2.l(macro)))/card(macro);
display meanerr1, meanerr2;
* Use the following code to specify that the column sums are known
* exactly. The errors are thus fixed to zero and two equations are
* dropped from the estimation procedure. The computational gains are
* that the constraints are all linear and the estimation problem is
* considerably smaller.
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ERR1.fx(ii) = 0.0;
W1.fx(ii,jwt) = WBAR1(ii,jwt);
Model SAMENTROP2 / SAMEQ
SAMMAKE
* ERROR1EQ
* SUMW1
ERROR2EQ
SUMW2
ENTROPY
ROWSUM
COLSUM
GDPFCDEF
GDPDEF /;
SAMENTROP2.holdFixed = 1;
solve SAMENTROP2 using nlp minimizing dentropy;
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