difference . Jika seluruh variabel sudah stasioner, maka data sudah bisa digunakan
untuk pengujian selanjutnya.
4.3. Vector Autoregression
Vector Autoregression dengan asumsi penggunaan lag 1 ditentukan oleh
stabilitas lag structure dengan menggunakan Inverse Roots of AR Characteristic Polynomial
dan prinsip Parsimony. Berdasarkan hasil peramalan Cadangan Devisa CD, Excess Reserve Bank ERB, Produk Domestik Bruto PDB, Suku Bunga
Pasar Uang SBPU, Kurs, Inflasi INF, Tingkat Bunga Kredit TBK, Net Ekspor NE dan Harga Relatif HR ditunjukkan pada lampiran hasil estimasi VAR sebagai
berikut:
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Tabel 4.9. Hasil Analisa VAR
Vector Autoregression Estimates Date: 083010 Time: 09:43
Sampleadjusted: 1985 2008 Included observations: 24 after adjusting endpoints
Standard errors in t-statistics in [ ]
LOGCD LOGERB LOGHR LOGINF LOGKURS LOGNE LOGPDB LOGSBPU LOGTBK LOGCD-1
0.295145 0.110806
0.328487 0.858985 0.375782
3.193326 -1.131296
1.547455 0.293429
0.28374 0.14699 0.13014 1.00260 0.40827
1.17141 0.37378 0.79098
0.23937 [ 1.04021] [ 0.75381] [ 2.52410] [ 0.85675]
[ 0.92043] [ 2.72605] [-3.02667]
[ 1.95639] [ 1.22584]
LOGERB-1 0.213497
1.105180 -0.205211 0.145078
0.014129 -2.264680
0.606516 -0.341141
0.031167 0.15430 0.07994 0.07077 0.54524
0.22202 0.63704 0.20327
0.43015 0.13017
[ 1.38364] [ 13.8254] [-2.89958] [ 0.26608] [ 0.06364]
[-3.55502] [ 2.98384] [-0.79308]
[ 0.23943] LOGHR-1
-0.261543 -0.318330
0.616327 1.144535 -0.453247
9.694700 1.177515
1.522593 -0.572229
0.61756 0.31994 0.28325 2.18220 0.88860
2.54961 0.81353 1.72158
0.52100 [-0.42351] [-0.99498] [ 2.17588] [ 0.52449]
[-0.51007] [ 3.80242] [ 1.44741]
[ 0.88442] [-1.09834]
LOGINF-1 0.086602
0.059361 0.035753 -0.525751
-0.150904 -0.735364
-0.282007 0.010863
0.030120 0.07365 0.03816 0.03378 0.26026
0.10598 0.30408 0.09703
0.20533 0.06214
[ 1.17578] [ 1.55567] [ 1.05832] [-2.02008] [-1.42388]
[-2.41830] [-2.90647] [ 0.05291]
[ 0.48473] LOGKURS-1 0.115153
-0.298758 0.022530 -0.353575
0.688767 5.411104
0.375082 0.077274
-0.345761 0.23498 0.12173 0.10778 0.83032
0.33811 0.97012 0.30955
0.65506 0.19824
[ 0.49005] [-2.45417] [ 0.20904] [-0.42583] [ 2.03710]
[ 5.57777] [ 1.21171] [ 0.11797]
[-1.74418] LOGNE-1
0.049693 0.032308
-0.002227 -0.071021 -0.022466
-0.554607 0.008655
-0.442061 0.037210
0.06427 0.03330 0.02948 0.22710 0.09248
0.26534 0.08467 0.17917
0.05422 [ 0.77318] [ 0.97031] [-0.07554] [-0.31272]
[-0.24294] [-2.09016] [ 0.10223]
[-2.46732] [ 0.68628]
LOGPDB-1 -0.220930
-0.218495 -0.041236 0.559120 0.015857
1.562613 0.505567
0.222198 -0.178893
0.19473 0.10088 0.08932 0.68810 0.28020
0.80395 0.25653 0.54285
0.16428 [-1.13454] [-2.16581] [-0.46168] [ 0.81256]
[ 0.05659] [ 1.94366] [ 1.97082]
[ 0.40931] [-1.08894]
LOGSBPU-1 -0.071437 0.112724
-0.031827 1.164647 0.416206
0.506339 -0.206432
0.562142 0.151783
0.10425 0.05401 0.04782 0.36837 0.15000
0.43040 0.13733 0.29062
0.08795 [-0.68525] [ 2.08716] [-0.66561] [ 3.16159]
[ 2.77463] [ 1.17644] [-1.50316]
[ 1.93430] [ 1.72582]
LOGTBK-1 -0.566522
-0.608469 0.352048 -1.464429
-0.545304 -0.867027
-0.833076 -1.072368
0.282024 0.44096 0.22845 0.20225 1.55816
0.63449 1.82050 0.58089
1.22926 0.37201
[-1.28475] [-2.66353] [ 1.74064] [-0.93985] [-0.85944]
[-0.47626] [-1.43414] [-0.87237]
[ 0.75811] C
8.722665 5.715363
0.518299 -15.07793 1.784261
-95.81371 5.131105
-13.17104 5.909052
6.55058 3.39363 3.00452 23.1470 9.42558
27.0442 8.62931 18.2611
5.52629 [ 1.33159] [ 1.68414] [ 0.17251] [-0.65140]
[ 0.18930] [-3.54285] [ 0.59461]
[-0.72126] [ 1.06926]
R-squared 0.970258
0.998533 0.964294 0.645103
0.967551 0.917375
0.943286 0.747641
0.670476 Adj. R-squared
0.951138 0.997590
0.941340 0.416954 0.946691
0.864259 0.906827
0.585411 0.458640
Sum sq. resids 0.256406
0.068817 0.053941 3.201522
0.530866 4.370364
0.444959 1.992615
0.182489 S.E. equation
0.135332 0.070111
0.062072 0.478205 0.194728
0.558721 0.178277
0.377266 0.114171
F-statistic 50.74557
1058.774 42.01016 2.827557
46.38316 17.27113
25.87245 4.608510
3.165065 Log likelihood
20.41405 36.19772
39.12047 -9.881393 11.68107
-13.61603 13.79939
-4.191249 24.49492
Akaike AIC -0.867838
-2.183143 -2.426706 1.656783 -0.140090
1.968003 -0.316616
1.182604 -1.207910
Schwarz SC -0.376982
-1.692287 -1.935850 2.147639 0.350766
2.458859 0.174240
1.673460 -0.717055
Mean dependent 9.856033
12.46419 4.698441 2.136693
8.275340 10.44096
13.70455 2.448420
2.794317 S.D. dependent
0.612227 1.428114
0.256287 0.626272 0.843391
1.516488 0.584051
0.585920 0.155171
Determinant Residual Covariance
4.43E-18 Log Likelihood d.f. adjusted 172.9998
Akaike Information Criteria -6.916652
Schwarz Criteria -2.498951
Sumber: Lampiran Eviews, 2010
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Berdasarkan hasil penelitian yang ditunjukkan pada Tabel 4.9 dengan menggunakan dasar lag 1 terlihat bahwa adanya hubungan antara CD, ERB, HR,
INF, Kurs, NE, PDB, SBPU dan TBK dengan lag 1, hal ini dapat disimpulkan bahwa dengan
mengamati t-statistik
dari masing-masing
koefisien, hubungan
interdependensi antara variabel CD, ERB, HR, INF, Kurs, NE, PDB, SBPU dan TBK secara statistik signifikan.
Berdasarkan hasil analisa Vector Autoregression diketahui bahwa variabel sebelumnya juga mempengaruhi variabel sekarang. Di mana dapat ditunjukkan pada
Tabel 4.9 bahwa variabel masa lalu t-1 berpengaruh signifikan terhadap dirinya sendiri dan variabel lain. Dari hasil estimasi tersebut di atas beserta uraiannya
ternyata hubungan timbal balik antara variabel CD, ERB, HR, INF, Kurs, NE, PDB, SBPU dan TBK menjadi semakin jelas dan dengan demikian hipotesa adanya
kontribusi timbal balik antara CD, ERB, HR, INF, Kurs, NE, PDB, SBPU dan TBK sebagai variabel yang diamati dalam penelitian ini terbukti. Model VAR sesuai
dengan ekspektasi perekonomian Indonesia di masa mendatang, hal tersebut dapat ditunjukkan pada trend beberapa variabel yang berfluktuasi. Variabel yang paling
banyak mempengaruhi yaitu ERB dan SBPU sebanyak 3 variabel sedangkan PDB tidak memiliki kontribusi terbesar pertama terhadap variabel lainnya.
4.4. Uji Kointegrasi dan Stabilitas Lag Struktur