BAB V PENUTUP
5.1. Kesimpulan Kesimpulan yang diperoleh dari penelitian ini adalah sebagai berikut : 1. Pendapatan nasional (Y) mempunyai pengaruh positif dan signifikan terhadap permintaan uang (M2) 2000:Q1 – 2008:Q2. 2. Tingkat inflasi (P) mempunyai pengaruh positif dan signifikan terhadap permintaan uang (M2) 2000:Q1 – 2008:Q2. 3. Tingkat suku bunga (R) tidak berpengaruh secara signifikan terhadap permintaan uang (M2) 2000:Q1 – 2008:Q2. Hasil yang diperoleh tidak sesuai dengan hipotesis.
5.2. Saran Untuk mengatasi meningkatnya permintaan uang (M2) di Indonesia karena adanya peningkatan tingkat inflasi sebaiknya Pemerintah lebih menjamin ketersediaan barang-barang pokok yang dibutuhkan masyarakat dan lebih bijak dalam mengambil kebijakan dalam hal menaikkan harga BBM, serta menjaga stabiitas nilai tukar rupiah terhadap mata uang asing terutama Dollar Amerika (US$). Pemerintah perlu menerapkan kebijakan anggaran surplus, dengan cara mengurangi pengeluarannya serta menaikkan jumlah pajak yang dipungut dari berbagai golongan.
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DAFTAR PUSTAKA
A. Buku. Boediono, (1984), Ekonomi Makro: Seri Sinopsis Pengantar Ilmu Ekonomi No.2, Edisi Keempat, Balai Penerbit Fakultas Ekonomi, UGM, Yogyakarta. ------------, (1985), Teori Pertumbuhan Ekonomi, Balai Penerbit Fakultas Ekonomi, UGM, Yogyakarta. -------------, (1994), Teori Pertumbuhan Ekonomi, Edisi Pertama, Balai Penerbit Fakultas Ekonomi, UGM, Yogyakarta. Gujarati, D. N, (2003), Basic Econometrics, 4th edition, McGraw-Hill International Editions, Singapore. Hariwijaya, M dan Djaelani. BM., (2006), Teknik Menulis Skripsi dan Thesis, Cetakan Ketiga, Zenith Publisher, Jakarta. Insukindro, (1993), Ekonomi Uang dan Bank, Edisi Pertama, BPFE UGM, Yogyakarta. Nopirin, Ph. D.,(1987), Ekonomi Moneter, Edisi Pertama, BPFE, Jakarta. Sugiyanto, Catur., (1995), Ekonometrika Terapan, Edisi Pertama, BPFE, Universitas Gajah Mada, Yogyakarta. Sukirno, Sadono., (2004), Pengantar Teori Makroekonomi, Edisi Kedua, P.T. Raja Grafindo Persada, Jakarta. Sumodiningrat, Gunawan, (1996), Ekonometrika Pengantar, BPFE UGM, Yogyakarta. Suparmoko, M ., (2002), Ekonomi Publik, Penerbit Andi Yogyakarta. Widarjono, A, (2005), Ekonometrika Teori dan Aplikasi Untuk Bisnis, Ekonisa, Yogyakarta.
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B.
Jurnal / Artikel dan Referensi Lainnya.
Bank Indonesia, Statistik Ekonomi dan Keuangan Indonesia, dalam beberapa tahun penerbitan. Belajar
ekonomi,
(2006),
“Pendapatan
Nasional”
diakses
dari
http://belajarekonomi.blogspot.com/2006/07/pendapatan-nasional.html. Tanggal 2 April 2009. Businessenvironment, (2006), “Menyimak Karakter Inflasi di Indonesia” diakses dari
http://businessenvironment.wordpress.com/2006/11/23/menyimak-
karakter-inflasi-di- indonesia/. Tanggal 2 April 2009. Insukindro dan Aliman., (1999), “Pemilihan dan Bentuk Fungsi Model Empirik: Studi Kasus Permintaan Uang Kartal Riil di Indonesia”, Jurnal Ekonomi dan Bisnis Indonesia, XIV(4), Oktober 1999, hal 49-61. Kirana, W dan Nurwadono, (1992), “Peran Pembangunan Sektor Keuangan Dalam Mobilisasi Dana dan Pertumbuhan Ekonomi”, Jurnal Ekonomi dan Bisnis Indonesia, No. 1 Tahun VII. Nopirin, (1998), “Analisis Permintaan Akan Uang Kas Di Indonesia 1976-1996”, Jurnal Ekonomi dan Bisnis Indonesia, Vol. 13, No. 2, hal 1-14. Pracoyo, A, (1998), “Analisis Jumlah Uang Beredar di Indonesia 1983-1994 Suatu Pendekatan dengan Uji Kointegrasi”, Media Ekonomi, Vol 4 No.1, hal 251-260. Prawoto, N, (2000), “Permintaan Uang di Indonesia Tahun 1976-1996 (Konsep Keynesian dan Monetaris dengan Pendekatan PAM)”, Jurnal Ekonomi Pembangunan, Vol. 5 No. 1, hal 37-52. Prayitno Lily, Heny Sandjaya, dan Richard Llewelyn, (2002), “Faktor-Faktor Yang Berpengaruh Terhadap Jumlah Uang Beredar di Indonesia Sebelum dan Sesudah Krisis: Sebuah Analisis Ekonometrika”, Jurnal Manajemen dan Kewirausahaan, Vol. 4, No.1, Maret 2002 : hal 46-55.
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LAMPIRAN 1 Data M2 2000
2001
2002
2003
2004
2005
2006
2007
2008
Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun. Sep. Dec. Mar. Jun.
656451 684335 686453 747028 766812 796440 783104 844053 831411 838635 859706 883908 877776 894213 911224 955692 935247 975166 986806 1033527 1020693 1073746 1150451 1203215 1195067 1253757 1291396 1382074 1375947 1451974 1512756 1643203 1586795 1699480
GNP konstan 2000 (Y) 319530.9 317687.5 332912.7 327477 344565.8 344407 353062.6 332159.7 357349.47 362759.27 374150.69 354599.93 385607.2 369596.1 382661.5 369564.9 383321 392644 403121.8 396961.8 408294.4 407408.8 419332.7 408397.6 426241.1 426286 444463.8 436444.5 449746.9 455216.1 475536.5 467233.6 486907.79 492654.22
IHK umum 204.34 208.24 211.87 221.37 226.04 233.46 239.44 249.15 98.39 99.26 100.88 104.44 105.44 106.19 107.27 109.83 110.83 113.44 114 116.86 120.59 121.86 124.33 136.86 139.57 140.79 142.42 145.89 148.67 151.11 152.32 155.5 160.81 110.08
IHK % perubahan (P) 0.94 1.8969051 1.7376347 4.4201746 2.0959428 3.2509777 2.5537172 4.0049153 3.47 0.8841756 1.6235764 3.4927977 0.9571297 0.7097497 1.014382 2.368015 0.9086215 2.3374042 0.4932289 2.4886691 3.1702397 1.0496994 2.0135119 9.9684809 1.9716308 0.8720265 1.1533505 2.418847 1.895183 1.6424128 0.8071021 2.0755132 3.3799818 4.44
2002=100 79.65882 80.42153 82.29444 84.32354 87.03952 89.23153 92.52083 95.38017 98.39 99.26 100.88 104.44 105.44 106.19 107.27 109.83 110.83 113.44 114 116.86 120.59 121.86 124.33 136.86 139.57 140.79 142.42 145.89 148.67 151.11 152.32 155.5 160.81 168.0446
Sumber : Statistik Ekonomi Dan Keuangan Indonesia (diolah sendiri)
R 12.4 11.69 12.84 13.24 14.86 15 16.16 17.24 17.02 15.85 14.36 13.63 12.9 11.55 8.58 7.14 6.11 6.31 6.61 6.71 6.93 7.19 8.51 11.75 12.19 11.7 11.05 9.71 8.52 7.87 7.44 7.42 7.26 7.49
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LAMPIRAN 2 Uji Mackinnon, White, dan Davidson (MWD) Model linear: M2 = α0 + α1Y + α2P + α3R + μ Model loglinear: LOGM2 = β0 + β1LOGY + β2P + β3R + μ 1. Meregres model linear Hasil regresi model linear : Dependent Variable: M2 Method: Least Squares Date: 08/23/04 Time: 02:16 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C Y P R
-1362769. 5.925364 16496.41 3774.421
151860.6 0.302407 6761.945 4357.592
-8.973812 19.59399 2.439595 0.866171
0.0000 0.0000 0.0208 0.3933
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.953862 0.949248 66109.11 1.31E+11 -423.4842 1.987505
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
1052604. 293451.3 25.14613 25.32570 206.7416 0.000000
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2. Meregres model loglinear Buat variabel LOGM2 (LOM2), LOGY(LOY) LOGM2= LOG(M2) LOGY = LOG(Y) Hasil regresi model loglinear : Dependent Variable: LOGM2 Method: Least Squares Date: 08/23/04 Time: 02:18 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY P R
-13.67683 2.131591 0.016056 0.001994
1.311265 0.099981 0.005591 0.003629
-10.43026 21.31994 2.871874 0.549415
0.0000 0.0000 0.0074 0.5868
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.962037 0.958240 0.054712 0.089804 52.67642 2.496792
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
13.83115 0.267737 -2.863319 -2.683747 253.4126 0.000000
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3. Untuk memilih model yang cocok kita harus mengestimasi persamaan berikut • Model linear: M2 = α0 + α1Y + α2P + α3R + α4Z1 + μ • Model loglinear: LOGM2 = y0 + y1LOGY + y2P + y3R + y4Z2 + μ Dimana: Z1 = LOG(F1)-F2 Z2 = ANTILOGF2-F1 F1 = M2-RES1 F2 = LOGM2-RES2 Hasil regresi model linear : Dependent Variable: M2 Method: Least Squares Date: 08/23/04 Time: 02:20 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C Y P R Z1
-1543111. 6.333815 13967.65 6010.814 -1378720.
143246.8 0.291432 5934.447 3853.081 422936.4
-10.77239 21.73340 2.353656 1.560002 -3.259874
0.0000 0.0000 0.0256 0.1296 0.0028
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.966235 0.961578 57521.15 9.60E+10 -418.1767 2.673390
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
1052604. 293451.3 24.89275 25.11721 207.4693 0.000000
Hasil regresi model loglinear : Dependent Variable: LOGM2 Method: Least Squares Date: 08/23/04 Time: 02:21 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY P R Z2
-14.32830 2.181823 0.014424 0.002878 5.79E-07
1.408123 0.107544 0.005709 0.003675 4.78E-07
-10.17546 20.28777 2.526317 0.783254 1.210473
0.0000 0.0000 0.0172 0.4398 0.2359
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.963863 0.958878 0.054293 0.085484 53.51437 2.613187
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
13.83115 0.267737 -2.853786 -2.629321 193.3733 0.000000
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Analisis : Kriteria Pengujian : • Jika Z1 tidak signifikan dan Z2 signifikan maka model linear yang cocok digunakan. • Jika Z2 tidak signifikan dan Z1 signifikan maka model loglinear yang cocok digunakan. Hasil pengujian : • Probabilitas t-hitung Z1 (0.0028) < 0.05 → signifikan • Probabilitas t-hitung Z2 (0.2359) > 0.05 → tidak signifikan Karena nilai Z1 signifikan dan Z2 tidak signifikan, maka model yang cocok digunakan adalah model loglinear.
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LAMPIRAN 3
TABULASI obs 2000:1 2000:2 2000:3 2000:4 2001:1 2001:2 2001:3 2001:4 2002:1 2002:2 2002:3 2002:4 2003:1 2003:2 2003:3 2003:4 2004:1 2004:2 2004:3 2004:4 2005:1 2005:2 2005:3 2005:4 2006:1 2006:2 2006:3 2006:4 2007:1 2007:2 2007:3 2007:4 2008:1 2008:2
LOGM2 13.39460 13.43620 13.43929 13.52386 13.55000 13.58791 13.57102 13.64597 13.63088 13.63953 13.66435 13.69211 13.68515 13.70370 13.72254 13.77019 13.74857 13.79036 13.80223 13.84849 13.83599 13.88666 13.95566 14.00051 13.99371 14.04166 14.07123 14.13910 14.13465 14.18843 14.22944 14.31216 14.27723 14.34583
LOGY 12.67461 12.66882 12.71564 12.69917 12.75004 12.74958 12.77440 12.71337 12.78647 12.80149 12.83241 12.77875 12.86257 12.82017 12.85491 12.82008 12.85663 12.88066 12.90699 12.89160 12.91974 12.91757 12.94642 12.92000 12.96276 12.96287 13.00462 12.98642 13.01644 13.02853 13.07220 13.05458 13.09583 13.10756
P 0.940000 1.896905 1.737635 4.420175 2.095943 3.250978 2.553717 4.004915 3.470000 0.884176 1.623576 3.492798 0.957130 0.709750 1.014382 2.368015 0.908621 2.337404 0.493229 2.488669 3.170240 1.049699 2.013512 9.968481 1.971631 0.872027 1.153350 2.418847 1.895183 1.642413 0.807102 2.075513 3.379982 4.440000
R 12.40000 11.69000 12.84000 13.24000 14.86000 15.00000 16.16000 17.24000 17.02000 15.85000 14.36000 13.63000 12.90000 11.55000 8.580000 7.140000 6.110000 6.310000 6.610000 6.710000 6.930000 7.190000 8.510000 11.75000 12.19000 11.70000 11.05000 9.710000 8.520000 7.870000 7.440000 7.420000 7.260000 7.490000
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LAMPIRAN 4
Multikolinearitas Dependent Variable: LOGY Method: Least Squares Date: 08/23/04 Time: 00:47 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C P R
13.11128 0.007554 -0.023390
0.057073 0.009951 0.004986
229.7268 0.759130 -4.691201
0.0000 0.4535 0.0001
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.415202 0.377473 0.098285 0.299459 32.20245 0.283470
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
12.87747 0.124569 -1.717791 -1.583112 11.00486 0.000245
Dependent Variable: P Method: Least Squares Date: 08/23/04 Time: 00:50 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY R
-30.31772 2.415920 0.141107
41.77148 3.182486 0.113808
-0.725799 0.759130 1.239875
0.4734 0.4535 0.2243
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.047289 -0.014176 1.757660 95.77040 -65.84899 1.995303
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
2.309000 1.745332 4.049941 4.184620 0.769364 0.471952
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Dependent Variable: R Method: Least Squares Date: 08/23/04 Time: 00:55 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY P
238.5449 -17.75007 0.334830
48.73474 3.783694 0.270051
4.894760 -4.691201 1.239875
0.0000 0.0001 0.2243
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.432474 0.395859 2.707521 227.2508 -80.53870 0.328861
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
10.74206 3.483396 4.914041 5.048720 11.81152 0.000154
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LAMPIRAN 5
Heteroskedastisitas White Heteroskedasticity Test: F-statistic Obs*R-squared
0.434366 4.762425
Probability Probability
0.902955 0.854507
Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 08/23/04 Time: 00:25 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY LOGY^2 LOGY*P LOGY*R P P^2 P*R R R^2
-9.654970 1.440281 -0.053610 -0.004905 -0.004603 0.064341 -2.78E-06 -7.72E-05 0.059055 1.37E-05
17.27360 2.628369 0.100016 0.007555 0.006381 0.099437 0.000174 0.000325 0.084071 0.000131
-0.558944 0.547975 -0.536019 -0.649243 -0.721346 0.647057 -0.015989 -0.237381 0.702440 0.105091
0.5814 0.5888 0.5969 0.5223 0.4777 0.5237 0.9874 0.8144 0.4892 0.9172
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.140071 -0.182402 0.004077 0.000399 144.7572 2.547718
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
0.002641 0.003750 -7.926892 -7.477962 0.434366 0.902955
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LAMPIRAN 6
Autokorelasi Breusch-Godfrey Serial Correlation LM Test: F-statistic Obs*R-squared
1.808941 5.690104
Probability Probability
0.169338 0.127700
Test Equation: Dependent Variable: RESID Method: Least Squares Date: 08/23/04 Time: 00:11 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY P R RESID(-1) RESID(-2) RESID(-3)
-0.067234 0.005212 -0.002134 0.000424 -0.145043 0.323051 -0.078668
1.343230 0.102478 0.005688 0.003571 0.200838 0.189865 0.222974
-0.050054 0.050858 -0.375118 0.118827 -0.722188 1.701476 -0.352811
0.9604 0.9598 0.7105 0.9063 0.4764 0.1003 0.7270
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.167356 -0.017676 0.052625 0.074774 55.78996 1.971945
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
-3.01E-15 0.052166 -2.869998 -2.555747 0.904471 0.506285
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LAMPIRAN 7
HASIL REGRESI AWAL Dependent Variable: LOGM2 Method: Least Squares Date: 08/23/04 Time: 00:22 Sample: 2000:1 2008:2 Included observations: 34 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LOGY P R
-13.67683 2.131591 0.016056 0.001994
1.311265 0.099981 0.005591 0.003629
-10.43026 21.31994 2.871874 0.549415
0.0000 0.0000 0.0074 0.5868
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.962037 0.958240 0.054712 0.089804 52.67642 2.496792
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)
13.83115 0.267737 -2.863319 -2.683747 253.4126 0.000000