Uji Kointegrasi Analisis dan Pembahasan
menentang penggunaan variabel difference, walaupun jika variabel tersebut memiliki unit root tidak stasioner pada level. Kedua pakar ini berargumen
bahwa differencing akan membuang informasi berharga yang terkait dengan pergerakan searah data. VAR in difference digunakan bagi data yang tidak
stasioner pada level dan tidak terkointegrasi. Dalam penelitian ini hampir semua data tidak stasioner pada level, namun semua data memiliki hubungan
kointegrasi, sehingga digunakan model VECM.
Tabel 4.11 Estimasi Vector Error Correction Model
Vector Error Correction Estimates Date: 031915 Time: 17:29
Sample adjusted: 2009M04 2013M12 Included observations: 57 after adjustments
Standard errors in t-statistics in [ ] Cointegrating Eq:
CointEq1 ROA_Y1-1
1.000000 MRB_X1-1
6.65E-06 3.3E-06
[ 2.00830] NPF_X2-1
0.198956 0.10526
[ 1.89021] FDR_X3-1
-0.058568 0.01255
[-4.66862] C
2.692692 Error Correction:
DROA_Y1 DMRB_X1
DNPF_X2 DFDR_X3
CointEq1 -0.423335
-494.1327 0.955870
6.366535 0.21778
880.659 0.34828
2.43390
[-1.94384] [-0.56109]
[ 2.74454] [ 2.61578]
DROA_Y1-1 -0.129183
158.6801 -0.323981
-2.834814 0.20832
842.384 0.33314
2.32812 [-0.62012]
[ 0.18837] [-0.97249]
[-1.21764] DROA_Y1-2
0.044215 940.5728
-0.351069 0.012085
0.16292 658.800
0.26054 1.82074
[ 0.27140] [ 1.42771]
[-1.34746] [ 0.00664]
DMRB_X1-1 3.14E-05
0.288340 1.25E-05
8.32E-05 3.7E-05
0.14803 5.9E-05
0.00041 [ 0.85667]
[ 1.94784] [ 0.21368]
[ 0.20336] DMRB_X1-2
-5.89E-06 0.435285
1.58E-05 0.000197
3.5E-05 0.14204
5.6E-05 0.00039
[-0.16768] [ 3.06446]
[ 0.28190] [ 0.50148]
DNPF_X2-1 -0.043668
414.1521 -0.351804
-0.392034 0.11010
445.225 0.17608
1.23048 [-0.39661]
[ 0.93021] [-1.99802]
[-0.31860] DNPF_X2-2
0.016148 171.1001
-0.298873 -1.261547
0.10575 427.608
0.16911 1.18179
[ 0.15271] [ 0.40013]
[-1.76733] [-1.06749]
DFDR_X3-1 -0.006174
33.03818 0.027919
-0.018572 0.01624
65.6758 0.02597
0.18151 [-0.38017]
[ 0.50305] [ 1.07492]
[-0.10232] DFDR_X3-2
-0.013397 -2.834173
0.005960 0.031661
0.01419 57.3831
0.02269 0.15859
[-0.94407] [-0.04939]
[ 0.26261] [ 0.19964]
C -0.046134
476.3672 -0.107016
-0.523992 0.05260
212.685 0.08411
0.58780 [-0.87715]
[ 2.23978] [-1.27230]
[-0.89144] R-squared
0.276134 0.509707
0.248719 0.233867
Adj. R-squared 0.137521
0.415821 0.104857
0.087161 Sum sq. Resids
2.091253 34196056
5.348359 261.1948
S.E. equation 0.210938
852.9805 0.337335
2.357400 F-statistic
1.992123 5.429001
1.728872 1.594120
Log likelihood 13.32121
-460.0597 -13.44104
-124.2626 Akaike AIC
-0.116534 16.49332
0.822493 4.710969
Schwarz SC 0.241896
16.85175 1.180923
5.069400 Mean dependent
-0.007719 1540.930
-0.044211 -0.052807
S.D. dependent 0.227133
1116.005 0.356546
2.467381 Determinant resid covariance dof adj.
12148.66 Determinant resid covariance
5615.914 Log likelihood
-569.5687