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## 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:

* 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;