hhfair.gms:
Reference:
- Fair, R C, Specification, Estimation, and Analysis of Macroeconomic Models. Harvard University Press, Cambridge, Mass, 1984.
- Original source: GAMS Model of hhfair.gms from GAMS Model Library
Point:
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* NLP written by GAMS Convert at 07/30/01 17:04:24
*
* Equation counts
* Total E G L N X
* 26 20 3 3 0 0
*
* Variable counts
* x b i s1s s2s sc si
* Total cont binary integer sos1 sos2 scont sint
* 30 30 0 0 0 0 0 0
* FX 2 2 0 0 0 0 0 0
*
* Nonzero counts
* Total const NL DLL
* 81 60 21 0
*
* Solve m using NLP minimizing objvar;
Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,objvar
,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30;
Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19
,e20,e21,e22,e23,e24,e25,e26;
e1.. - x25*x26**0.944*x27**0.891136 - objvar =E= 0;
e2.. - 0.01*sqr(0.5*x5**0.5 + 0.5*(1004.72366 + (-x8) - x15)**0.5) + x25
=E= 0;
e3.. - 0.01*sqr(0.5*x6**0.5 + 0.5*(1004.72366 + (-x9) - x16)**0.5) + x26
=E= 0;
e4.. - 0.01*sqr(0.5*x7**0.5 + 0.5*(1004.72366 + (-x10) - x17)**0.5) + x27
=E= 0;
e5.. - 0.07*x2 - x8 + x28 =E= 0;
e6.. - 0.07*x3 - x9 + x29 =E= 0;
e7.. - 0.07*x4 - x10 + x30 =E= 0;
e8.. x22 - 0.2*x28 =E= 0;
e9.. x23 - 0.2*x29 =E= 0;
e10.. x24 - 0.2*x30 =E= 0;
e11.. x5 + x19 + x22 - x28 =E= 0;
e12.. x6 + x20 + x23 - x29 =E= 0;
e13.. x7 + x21 + x24 - x30 =E= 0;
e14.. x1 - x2 + x11 - x12 + x19 =E= 0;
e15.. x2 - x3 + x12 - x13 + x20 =E= 0;
e16.. x3 - x4 + x13 - x14 + x21 =E= 0;
e17.. x15*(x12 - 0.255905*x5) =E= 1;
e18.. x16*(x13 - 0.255905*x6) =E= 1;
e19.. x17*(x14 - 0.255905*x7) =E= 1;
e20.. x4 + x14 =E= 1100;
e21.. - 0.25846405*x5 + x12 =G= 0;
e22.. - 0.25846405*x6 + x13 =G= 0;
e23.. - 0.25846405*x7 + x14 =G= 0;
e24.. x8 + x15 =L= 904.251294;
e25.. x9 + x16 =L= 904.251294;
e26.. x10 + x17 =L= 904.251294;
* set non default bounds
x1.fx = 1000;
x5.lo = 100;
x6.lo = 100;
x7.lo = 100;
x8.lo = 100; x8.up = 400;
x9.lo = 100; x9.up = 400;
x10.lo = 100; x10.up = 400;
x11.fx = 100;
x25.lo = 0.01;
x26.lo = 0.01;
x27.lo = 0.01;
* set non default levels
x2.l = 1000;
x3.l = 1000;
x4.l = 1000;
x8.l = 400;
x9.l = 400;
x10.l = 400;
x12.l = 100;
x13.l = 100;
x14.l = 100;
x25.l = 1;
x26.l = 1;
x27.l = 1;
* set non default marginals
Model m / all /;
m.limrow=0; m.limcol=0;
$if NOT '%gams.u1%' == '' $include '%gams.u1%'
Solve m using NLP minimizing objvar;
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