Saran Penelitian dan Kebijakan
LAMPIRAN
Lampiran 1 Hasil pengolahan estimasi model permintaan energi rumah tangga di Pulau Jawa dengan model LA-AIDS tahun 2007 – 2010
The SAS System 14:23 Friday, June 4, 2011 1
The SYSLIN Procedure Ordinary Least Squares Estimation
Model A Dependent Variable W1
Label W1 Analysis of Variance
Sum of Mean Source DF Squares Square F Value Pr F
Model 9 1619173 179908.1 19776.8 .0001 Error 120558 1096707 9.096925
Corrected Total 120567 2715880 Root MSE 3.01611 R‐Square 0.59619
Dependent Mean 0.56790 Adj R‐Sq 0.59616 Coeff Var 531.09498
Parameter Estimates Parameter Standard Variable
Variable DF Estimate Error t Value Pr |t| Label Intercept 1 1.263139 0.004464 282.94 .0001 Intercept
LNP1 1 0.017238 0.000585 29.45 .0001 LNP1 LNP2 1 ‐0.00174 0.000168 ‐10.40 .0001 LNP2
LNP3 1 ‐0.00272 0.000182 ‐14.94 .0001 LNP3 LNP4 1 0.000521 0.000118 4.41 .0001 LNP4
LNP5 1 0.002256 0.000141 15.96 .0001 LNP5 LNP6 1 ‐0.11047 0.000353 ‐313.14 .0001 LNP6
LNYP 1 ‐0.00613 0.000380 ‐16.13 .0001 LNYP D1 1 ‐0.01002 0.000581 ‐17.25 .0001 D1
T 1 0.007616 0.000306 24.85 .0001 T The
SAS System 14:23 Friday, June 4, 2011 2 The SYSLIN Procedure
Ordinary Least Squares Estimation Model B
Dependent Variable W2 Label W2
Analysis of Variance Sum of Mean
Source DF Squares Square F Value Pr F Model 9 12726.58 1414.064 3916.03 .0001
Error 120558 43533.10 0.361097 Corrected Total 120567 56259.68
58
Root MSE 0.60091 R‐Square 0.22621 Dependent Mean 0.02973 Adj R‐Sq 0.22615
Coeff Var 2021.38279 Parameter Estimates
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
Intercept 1 0.000725 0.000889 0.81 0.4152 Intercept LNP1 1 0.007855 0.000117 67.35 .0001 LNP1
LNP2 1 0.000969 0.000033 29.02 .0001 LNP2 LNP3 1 ‐1.67E‐6 0.000036 ‐0.05 0.9632 LNP3
LNP4 1 0.000501 0.000024 21.28 .0001 LNP4 LNP5 1 0.001418 0.000028 50.34 .0001 LNP5
LNP6 1 ‐0.00622 0.000070 ‐88.53 .0001 LNP6 LNYP 1 0.013118 0.000076 173.21 .0001 LNYP
D1 1 0.002656 0.000116 22.95 .0001 D1 T 1 0.000880 0.000061 14.42 .0001 T
The SAS System 14:23 Friday, June 4, 2011 3
The SYSLIN Procedure Ordinary Least Squares Estimation
Model C Dependent Variable W3
Label W3 Analysis of Variance
Sum of Mean Source DF Squares Square F Value Pr F
Model 9 9996.414 1110.713 7218.95 .0001 Error 120558 18549.15 0.153861
Corrected Total 120567 28545.56 Root MSE 0.39225 R‐Square 0.35019
Dependent Mean 0.00752 Adj R‐Sq 0.35014 Coeff Var 5218.28527
Parameter Estimates Parameter Standard Variable
Variable DF Estimate Error t Value Pr |t| Label Intercept 1 ‐0.02677 0.000581 ‐46.10 .0001 Intercept
LNP1 1 0.004026 0.000076 52.89 .0001 LNP1 LNP2 1 0.000261 0.000022 11.95 .0001 LNP2
LNP3 1 ‐0.00279 0.000024 ‐117.59 .0001 LNP3 LNP4 1 0.001020 0.000015 66.38 .0001 LNP4
LNP5 1 0.001210 0.000018 65.81 .0001 LNP5 LNP6 1 ‐0.00227 0.000046 ‐49.47 .0001 LNP6
LNYP 1 0.005359 0.000049 108.40 .0001 LNYP D1 1 0.000657 0.000076 8.70 .0001 D1
T 1 0.003210 0.000040 80.55 .0001 T The
SAS System 14:23 Friday, June 4, 2011 4
59
The SYSLIN Procedure Ordinary Least Squares Estimation
Model D Dependent Variable W4
Label W4 Analysis of Variance
Sum of Mean Source DF Squares Square F Value Pr F
Model 9 26379.58 2931.065 7205.69 .0001 Error 120558 49039.50 0.406771
Corrected Total 120567 75419.08 Root MSE 0.63779 R‐Square 0.34977
Dependent Mean 0.01545 Adj R‐Sq 0.34972 Coeff Var 4128.43476
Parameter Estimates Parameter Standard Variable
Variable DF Estimate Error t Value Pr |t| Label Intercept 1 0.033560 0.000944 35.55 .0001 Intercept
LNP1 1 0.004569 0.000124 36.91 .0001 LNP1 LNP2 1 0.000664 0.000035 18.73 .0001 LNP2
LNP3 1 0.001935 0.000039 50.24 .0001 LNP3 LNP4 1 ‐0.00215 0.000025 ‐86.11 .0001 LNP4
LNP5 1 0.001567 0.000030 52.43 .0001 LNP5 LNP6 1 ‐0.00786 0.000075 ‐105.41 .0001 LNP6
LNYP 1 0.012474 0.000080 155.18 .0001 LNYP D1 1 0.005358 0.000123 43.62 .0001 D1
T 1 ‐0.00530 0.000065 ‐81.83 .0001 T The
SAS System 14:23 Friday, June 4, 2011 5 The SYSLIN Procedure
Ordinary Least Squares Estimation Model E
Dependent Variable W5 Label W5
Analysis of Variance Sum of Mean
Source DF Squares Square F Value Pr F Model 9 97508.92 10834.32 15682.8 .0001
Error 120558 83286.59 0.690842 Corrected Total 120567 180795.5
Root MSE 0.83117 R‐Square 0.53933 Dependent Mean 0.02317 Adj R‐Sq 0.53930
Coeff Var 3587.71582 Parameter Estimates
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
Intercept 1 ‐0.11040 0.001230 ‐89.74 .0001 Intercept LNP1 1 0.016630 0.000161 103.09 .0001 LNP1
LNP2 1 0.000833 0.000046 18.02 .0001 LNP2
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LNP3 1 0.002738 0.000050 54.56 .0001 LNP3 LNP4 1 0.001857 0.000033 57.01 .0001 LNP4
LNP5 1 ‐0.00232 0.000039 ‐59.61 .0001 LNP5 LNP6 1 ‐0.00434 0.000097 ‐44.65 .0001 LNP6
LNYP 1 0.031189 0.000105 297.74 .0001 LNYP D1 1 ‐0.00808 0.000160 ‐50.47 .0001 D1
T 1 ‐0.00097 0.000084 ‐11.43 .0001 T The
SAS System 14:23 Friday, June 4, 2011 6 The SYSLIN Procedure
Seemingly Unrelated Regression Estimation Cross Model Covariance
A B C D E A
9.09692 ‐.495156 ‐.110427 ‐.169363 ‐.568888 B
‐0.49516 0.361097 ‐.013327 ‐.065929 ‐.209042 C
‐0.11043 ‐.013327 0.153861 ‐.066280 ‐.058430 D
‐0.16936 ‐.065929 ‐.066280 0.406771 ‐.231441 E
‐0.56889 ‐.209042 ‐.058430 ‐.231441 0.690842 Cross Model Correlation
A B C D E A
1.00000 ‐0.27320 ‐0.09334 ‐0.08804 ‐0.22693 B
‐0.27320 1.00000 ‐0.05654 ‐0.17203 ‐0.41854 C
‐0.09334 ‐0.05654 1.00000 ‐0.26494 ‐0.17922 D
‐0.08804 ‐0.17203 ‐0.26494 1.00000 ‐0.43659 E
‐0.22693 ‐0.41854 ‐0.17922 ‐0.43659 1.00000 Cross Model Inverse Correlation
A B C D E A
2.69093 2.45920 1.54131 2.35349 2.94366 B
2.45920 3.99366 2.00731 3.17006 3.97332 C
1.54131 2.00731 2.30412 2.21309 2.56906 D
2.35349 3.17006 2.21309 4.10739 4.05074 E
2.94366 3.97332 2.56906 4.05074 5.55992 Cross Model Inverse Covariance
A B C D E A
0.29581 1.3569 1.3028 1.2235 1.17422 B
1.35686 11.0598 8.5161 8.2714 7.95523 C
1.30280 8.5161 14.9754 8.8463 7.87990 D
1.22346 8.2714 8.8463 10.0976 7.64135 E
1.17422 7.9552 7.8799 7.6413 8.04803 System Weighted MSE 1.0554
Degrees of freedom 602805 System Weighted R‐Square 0.7986
The SAS System 14:23 Friday, June 4, 2011 7
The SYSLIN Procedure Seemingly Unrelated Regression Estimation
Model A Dependent Variable W1
Label W1 Parameter Estimates
61
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
Intercept 1 0.722165 0.001278 565.03 .0001 Intercept LNP1 1 0.085681 0.000360 238.31 .0001 LNP1
LNP2 1 0.003739 0.000065 57.66 .0001 LNP2 LNP3 1 0.003264 0.000045 71.83 .0001 LNP3
LNP4 1 0.004494 0.000061 73.46 .0001 LNP4 LNP5 1 0.007581 0.000080 94.50 .0001 LNP5
LNP6 1 ‐0.10476 0.000309 ‐339.43 .0001 LNP6 LNYP 1 0.004056 0.000340 11.92 .0001 LNYP
D1 1 ‐0.03448 0.000542 ‐63.58 .0001 D1 T 1 0.015687 0.000248 63.31 .0001 T
Model B Dependent Variable W2
Label W2 Parameter Estimates
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
Intercept 1 0.027757 0.000295 94.20 .0001 Intercept LNP1 1 0.003739 0.000065 57.66 .0001 LNP1
LNP2 1 0.000457 0.000029 15.59 .0001 LNP2 LNP3 1 0.000151 0.000017 9.02 .0001 LNP3
LNP4 1 0.000301 0.000019 16.19 .0001 LNP4 LNP5 1 0.000991 0.000023 43.73 .0001 LNP5
LNP6 1 ‐0.00564 0.000057 ‐98.58 .0001 LNP6 LNYP 1 0.012208 0.000071 172.83 .0001 LNYP
D1 1 0.003802 0.000108 35.07 .0001 D1 T 1 0.000542 0.000052 10.39 .0001 T
Model C Dependent Variable W3
Label W3 The
SAS System 14:23 Friday, June 4, 2011 8 The SYSLIN Procedure
Seemingly Unrelated Regression Estimation Parameter Estimates
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
Intercept 1 ‐0.01248 0.000227 ‐55.12 .0001 Intercept LNP1 1 0.003264 0.000045 71.83 .0001 LNP1
LNP2 1 0.000151 0.000017 9.02 .0001 LNP2 LNP3 1 ‐0.00252 0.000019 ‐130.33 .0001 LNP3
LNP4 1 0.001200 0.000013 91.69 .0001 LNP4 LNP5 1 0.001218 0.000016 75.51 .0001 LNP5
LNP6 1 ‐0.00331 0.000040 ‐82.49 .0001 LNP6 LNYP 1 0.005863 0.000046 126.22 .0001 LNYP
D1 1 0.001575 0.000072 22.00 .0001 D1 T 1 0.003495 0.000035 100.67 .0001 T
Model D
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Dependent Variable W4 Label W4
Parameter Estimates Parameter Standard Variable
Variable DF Estimate Error t Value Pr |t| Label Intercept 1 0.009951 0.000255 39.00 .0001 Intercept
LNP1 1 0.004494 0.000061 73.46 .0001 LNP1 LNP2 1 0.000301 0.000019 16.19 .0001 LNP2
LNP3 1 0.001200 0.000013 91.69 .0001 LNP3 LNP4 1 ‐0.00255 0.000022 ‐116.05 .0001 LNP4
LNP5 1 0.001609 0.000021 77.96 .0001 LNP5 LNP6 1 ‐0.00506 0.000053 ‐94.61 .0001 LNP6
LNYP 1 0.011077 0.000072 153.61 .0001 LNYP D1 1 0.003545 0.000115 30.88 .0001 D1
T 1 ‐0.00645 0.000052 ‐123.31 .0001 T Model E
Dependent Variable W5 Label W5
The SAS System 14:23 Friday, June 4, 2011 9
The SYSLIN Procedure Seemingly Unrelated Regression Estimation
Parameter Estimates Parameter Standard Variable
Variable DF Estimate Error t Value Pr |t| Label Intercept 1 ‐0.01497 0.000321 ‐46.57 .0001 Intercept
LNP1 1 0.007581 0.000080 94.50 .0001 LNP1 LNP2 1 0.000991 0.000023 43.73 .0001 LNP2
LNP3 1 0.001218 0.000016 75.51 .0001 LNP3 LNP4 1 0.001609 0.000021 77.96 .0001 LNP4
LNP5 1 ‐0.00316 0.000035 ‐90.05 .0001 LNP5 LNP6 1 ‐0.00824 0.000069 ‐119.68 .0001 LNP6
LNYP 1 0.030763 0.000095 322.69 .0001 LNYP D1 1 ‐0.00351 0.000149 ‐23.53 .0001 D1
T 1 ‐0.00159 0.000068 ‐23.42 .0001 T Parameter Estimates
Parameter Standard Variable Variable DF Estimate Error t Value Pr |t| Label
RESTRICT ‐1 ‐94866.5 2783.274 ‐34.08 .0001 RESTRICT ‐1 738447.9 17440.14 42.34 .0001
RESTRICT ‐1 1298911 19591.95 66.30 .0001 RESTRICT ‐1 300907.1 17440.72 17.25 .0001
RESTRICT ‐1 1103857 14868.36 74.24 .0001 RESTRICT ‐1 ‐166405 7243.311 ‐22.97 .0001
RESTRICT ‐1 266445.4 7779.268 34.25 .0001 RESTRICT ‐1 ‐341655 9120.331 ‐37.46 .0001
RESTRICT ‐1 104209.9 7706.557 13.52 .0001 RESTRICT ‐1 835351.5 40129.47 20.82 .0001
RESTRICT ‐1 ‐572061 34947.50 ‐16.37 .0001 RESTRICT ‐1 623985.7 28265.93 22.08 .0001
RESTRICT ‐1 ‐1943504 38130.13 ‐50.97 .0001 RESTRICT ‐1 ‐769161 33867.41 ‐22.71 .0001
RESTRICT ‐1 1141196 31499.94 36.23 .0001
ABSTRACT
DIANA BHAKTI. Household Energy Demand in Java. Supervised under SRI HARTOYO and MUHAMMAD FIRDAUS
Reducing of subsidies would increase energy prices that affect the level of energy consumption and consumer welfare. Analyze the behavior of households in
Java in consuming energy was the aim of this study, this include their price elasticity, income elasticity, and cross elasticity of energy commodities. The
methode of this study is the linear approximate version of the almost ideal demand system LA-AIDS model using data from the National Socio-Economic Survey
SUSENAS covering the period from 2007 to 2010 for household in Java along with the kerosene’s conversion to gas program undertaken by the government. The
own price elasticities of energy except for the electricity showed that they are elastic so the increase of their price will effectively reducing its consumption.
While the cross elasticities showed that the energy comodities are substitute each other, but in very low level. The kerosene’s conversion to gas has been shifting
household kerosene consumption in Java into commodities LPG, city gas, and coal. Keywords : energy demand, LA-AIDS, SUR
I. PENDAHULUAN