* Model RAS Tabel IO NATUNA Tahun 2017 * Basis data : Tabel IO updating KEPRI NATUNA Tahun 2017 * Struktur Tabel RAS : 17 sektor * Disusun oleh : Meirina Anggraeni * PWL - IPB SETS i sektor input antara/1*17/; ALIAS (i,j); SCALAR TotM Total Impor NATUNA 2017/17019.57/ TotF Total Final Demand NATUNA 2017/36624.53/ TotV Total PDRB NATUNA 2017/19604.96/; PARAMETERS Q2017(j) total input tabel I-O Dugaan NATUNA 2017 17 sektor/ 1 2381.41 2 1627.53 3 19899.96 4 290.58 5 20.79 6 1.20 7 2177.28 8 733.74 9 147.03 10 437.52 11 116.66 12 44.30 13 569.90 14 661.88 15 62.01 16 362.63 17 25.97 / PDRB2017(j) PDRB NATUNA tiap sektor tahun 2017/ 1 525.73 2 1590.39 3 14227.34 4 151.17 5 16.37 6 1.17 7 1480.21 8 652.54 9 92.99 10 141.96 11 115.7 12 26.82 13 121.18 14 352.72 15 59.58 16 36.16 17 12.94 /; TABLE A2017(i,j) Koefisien Teknis NATUNA Tahun 2017 17 Sektor 1 2 3 4 5 6 7 8 1 0.0318 0.0413 0.0000 0.0140 0.0000 0.0000 0.0000 0.0000 2 0.0000 0.1127 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3 0.0000 0.0000 0.0000 0.0023 0.2329 0.0000 0.0488 0.0000 4 0.8908 0.4460 0.6944 0.7408 0.4418 0.7315 0.8072 0.5538 5 0.0000 0.0000 0.0273 0.0116 0.1188 0.0255 0.0003 0.0291 6 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 7 0.0013 0.0000 0.0000 0.0000 0.0000 0.0000 0.0023 0.0000 8 0.0349 0.0209 0.0221 0.0254 0.0250 0.0222 0.0280 0.0185 9 0.0000 0.0000 0.0002 0.0003 0.0003 0.0000 0.0000 0.0012 10 0.0186 0.2379 0.2295 0.1714 0.0000 0.0213 0.0000 0.0297 11 0.0000 0.0000 0.0008 0.0005 0.0021 0.0146 0.0012 0.0027 12 0.0020 0.0170 0.0043 0.0066 0.0082 0.0758 0.0363 0.1160 13 0.0066 0.0756 0.0142 0.0166 0.1569 0.0987 0.0734 0.0366 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 15 0.0000 0.0000 0.0000 0.0000 0.0005 0.0030 0.0000 0.0000 16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 17 0.0130 0.0486 0.0073 0.0106 0.0135 0.0074 0.0023 0.2123 + 9 10 11 12 13 14 15 16 1 0.2024 0.0000 0.0000 0.0000 0.0000 0.0021 0.0000 0.0000 2 0.0410 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 4 0.2719 0.5760 0.3310 0.0444 0.2710 0.9125 0.7646 0.0329 5 0.0044 0.0032 0.1557 0.0003 0.0778 0.0002 0.0832 0.0089 6 0.0059 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0017 7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 8 0.0208 0.0179 0.0102 0.0014 0.0082 0.0340 0.0253 0.0013 9 0.0000 0.0024 0.0000 0.0313 0.0132 0.0017 0.0000 0.7327 10 0.0000 0.1575 0.3878 0.0338 0.2814 0.0042 0.0026 0.0017 11 0.0004 0.0028 0.0677 0.0004 0.0217 0.0447 0.0321 0.0035 12 0.0071 0.0928 0.0000 0.8137 0.0000 0.0000 0.0000 0.0000 13 0.3616 0.0860 0.0398 0.0469 0.1858 0.0000 0.0000 0.0400 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0697 0.0000 15 0.0000 0.0000 0.0000 0.0004 0.0000 0.0006 0.0128 0.0000 16 0.0230 0.0000 0.0000 0.0030 0.0000 0.0000 0.0000 0.1688 17 0.0614 0.0610 0.0079 0.0244 0.1409 0.0000 0.0096 0.0085 + 17 1 0.0000 2 0.0000 3 0.0000 4 0.7799 5 0.0405 6 0.0021 7 0.0000 8 0.0248 9 0.0000 10 0.0400 11 0.0067 12 0.0000 13 0.0000 14 0.0000 15 0.0000 16 0.0000 17 0.1059 PARAMETERS TB(i) Original estimate for sectoral Total Output or Input cells 2017 QB(i,j) Original estimate for intersectoral IO transaction cells 2017 VB(j) Original estimate for sectoral Value Added cells 2017 MB(j) Original estimate for sectoral Import cells 2017 FB(i) Original estimate for sectoral Final Demand cells 2017 TW(i) Weight for sectoral Total Output or Input cells QW(i,j) Weight for sectoral intersectoral IO transaction cells VW(j) Weight for sectoral Value Added cells MW(j) Weight for sectoral Import cells FW(i) Weight for sectoral Final Demand cells; TB(i) = Q2017(i); QB(i,j) = A2017(i,j)*TB(j); VB(j) = PDRB2017(j); MB(j) = TB(j)-VB(j)- Sum(i,QB(i,j)); FB(i) = TB(i)-Sum(j,QB(i,j)); TW(i)$(TB(i) GT 0) = 1; QW(i,j)$(QB(i,j) GT 0) = 1; VW(j)$(VB(j) GT 0) = 1; MW(j)$(MB(j) GT 0) = 1; FW(i)$(FB(i) GT 0) = 1; TW(i)$(TB(i) EQ 0) = 0; QW(i,j)$(QB(i,j) EQ 0) = 0; VW(j)$(VB(j) EQ 0) = 0; MW(j)$(MB(j) EQ 0) = 0; FW(i)$(FB(i) EQ 0) = 0; VARIABLES SSDEV Sum of Squared Deviation estimating Information Gain T(i) Optimal estimates for Sectoral Total Output or Input cells 2017 Q(i,j) Optimal estimates for Intersectoral Transaction cells 2017 M(j) Optimal estimates for Sectoral Import cells 2017 F(i) Optimal estimates for Sectoral Final Demand cells 2017 FM Optimal estimates for Final Demand for Import cells 2017 FF Optimal estimates for Final Demand for Final Demand cells 2017; POSITIVE VARIABLES T,Q,M,F,FM,FF; EQUATIONS OBJ Objective Function CBal(j) Column Balance Constraint Function RBal(i) Row Balance Constraint Function TBal total Balance Constraint Function TM Total Import Constraint Function TF Total Final Demand Constraint Function; OBJ .. SSDEV=E=Sum((i,j)$ (QW(i,j) GT 0), QW(i,j)* SQR(Q(i,j)- QB(i,j) )/QB(i,j))+ Sum((i)$ (TW(i) GT 0), TW(i)* SQR(T(i)- TB(i))/TB(i)) + Sum((j)$ (MW(j) GT 0), MW(j)* SQR(M(j)- MB(j))/MB(j)) + Sum((i)$ (FW(i) GT 0), FW(i)* SQR(F(i)- FB(i))/FB(i)); CBal(j) ..T(j)=E=Sum(i,Q(i,j)$(QB(i,j) GT 0))+ M(j)$ (MB(j) GT 0) + VB(j); RBal(i) ..T(i)=E=Sum(j,Q(i,j)$(QB(i,j) GT 0))+ F(i); TM ..Sum(j,M(j)$ (MB(j) GT 0))+FM=E=TotM; TF ..Sum(i,F(i))+FF=E=TotF; TBal ..TotV+TotM - TotF=E=0; MODEL ModelRAS/ALL/; Q.L(i,j)=QB(i,j)$QW(i,j) ; T.L(i)=TB(i)$TW(i) ; M.L(i)=MB(i)$MW(i) ; F.L(i)=FB(i)$FW(i) ; OPTION NLP = MINOS5 ; OPTION RESLIM = 9000 ; OPTION ITERLIM = 100000 ; SOLVE ModelRAS USING NLP MINIMIZING SSDEV; SETS Item/MP2017,FD2017,TO2017 /; PARAMETERS HslL (i,Item) Tabel Hasil Level Optimal HslM (i,Item) Tabel Hasil Marginal Value ; HslL(i,"MP2017")=M.L(i) ; HslL(i,"FD2017")=F.L(i) ; HslL(i,"TO2017")=T.L(i) ; HslM(i,"MP2017")=M.L(i); HslM(i,"FD2017")=F.L(i); HslM(i,"TO2017")=T.L(i); DISPLAY Q.L, Q.M, T.L, M.L, F.L, HslL, HslM, FM.L, FM.M, FF.L, FF.M;