hasil dan interpretasi
DESCRIPTION
interpretasi dataTRANSCRIPT
Kuis Ekonometrik
Nama : Suci Safitriani
NIM : 09.6147
Kelas : 4 SE3
Soal Quiz 3
Data yang digunakan adalah data yang berasal dari 609 perusahaan yang
dengan series waktu 1998-2008. Variabel-variabel yang digunakan terdiri atas nilai
produksi, tenaga kerja, capital dan bahan baku. Dan dalam pengolahannya, variabel-
variabel tersebut di logaritma naturalkan. Berikut ini adalah hasil pengolahan dengan
menggunakan eviews :
1. Uji Unit Root
Uji ini digunakan untuk melihat kestasioner data yang digunakan.
Hipotesis :
Ho : ρ¿=1
H 1: ρ¿<1
Output :
Group unit root test: Summary Series: LNY_30_1Date: 11/22/12 Time: 14:50Sample: 1998 2008Exogenous variables: Individual effectsAutomatic selection of maximum lagsAutomatic selection of lags based on SIC: 0 to 1Newey-West bandwidth selection using Bartlett kernel
Cross-Method Statistic Prob.** sections ObsNull: Unit root (assumes common unit root process) Levin, Lin & Chu t* -1504.01 0.0000 2422 23800
Null: Unit root (assumes individual unit root process) Im, Pesaran and Shin W-stat -70.0799 0.0000 2422 23800ADF - Fisher Chi-square 10124.2 0.0000 2422 23800PP - Fisher Chi-square 11619.5 0.0000 2422 24220
** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.
Keputusan : Tolak Ho, karena 0,00<0,05
Kesimpulan : Dengan tingkat kepercayaan 95% dapat disimpulkan bahwa data
sudah bersifat stasioner.
Berdasarkan output di atas dapat dilihat bahwa nilai probabilitas = 0,000 sehingga
dapat dikatakan bahwa data sudah stasioner sehingga bisa dilanjutkan ke proses
pengolahan selanjutnya.
2. Estimasi Model
Model Pooled
Model Common effect merupakan teknik yang paling sederhana untuk
mengestimasi data panel, yaitu dengan mengkombinasikan data time series dan cross
section dengan metode Ordinary Least Square (OLS). Dalam pendekatan ini, tidak
memperhatikan dimensi individu maupun waktu. Sehingga diasumsikan intersep dan
slope antar individu tetap sepanjang waktu dan individu.
Persamaan Umum : Y ¿=α+ β ' X ¿+ε ¿
Output :
Dependent Variable: LNY_30?
Method: Pooled Least Squares
Date: 11/22/12 Time: 15:03
Sample: 1998 2008
Included observations: 11
Cross-sections included: 609
Total pool (balanced) observations: 6699
Variable Coefficient Std. Error t-Statistic Prob.
C 3.923850 0.179383 21.87414 0.0000
LNL_30? 0.336357 0.043808 7.677954 0.0000
LNK_30? 0.035424 0.012361 2.865730 0.0042
LNM_30? 0.616664 0.004774 129.1794 0.0000
LNK_30?*LNL_30? 0.004378 0.002972 1.473101 0.1408
R-squared 0.869777 Mean dependent var 14.55332
Adjusted R-squared 0.869699 S.D. dependent var 1.645467
S.E. of regression 0.593968 Akaike info criterion 1.796763
Sum squared resid 2361.629 Schwarz criterion 1.801846
Log likelihood -6013.259 Hannan-Quinn criter. 1.798519
F-statistic 11177.52 Durbin-Watson stat 0.531390
Prob(F-statistic) 0.000000
Model Fixed (FEM)
Pendekatan Model Fixed Effect mengasumsikan bahwa perbedaan antar individu
dapat diakomodasi melalui perbedaan intersepnya. Model ini didasarkan oleh adanya
perbedaan intersep antara individu, namun intersepnya sama antar waktu (time
invariant). Disamping itu, model ini juga mengasumsikan bahwa koefisien regresi
(slope) tetap antar individu dan antar waktu.
Persamaan Umum : Y ¿=α i+ β ' X¿+ε¿
Output :
Dependent Variable: LNY_30?Method: Pooled Least SquaresDate: 11/22/12 Time: 15:03Sample: 1998 2008Included observations: 11Cross-sections included: 609
Total pool (balanced) observations: 6699
Variable Coefficient Std. Error t-Statistic Prob.
C 5.700524 0.155975 36.54767 0.0000LNL_30? 0.248196 0.036903 6.725620 0.0000LNK_30? -0.011129 0.009460 -1.176420 0.2395LNM_30? 0.575883 0.006194 92.96837 0.0000
LNK_30?*LNL_30? -0.000710 0.002324 -0.305684 0.7599
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.959743 Mean dependent var 14.55332Adjusted R-squared 0.955695 S.D. dependent var 1.645467S.E. of regression 0.346352 Akaike info criterion 0.804321Sum squared resid 730.0735 Schwarz criterion 1.427452Log likelihood -2081.074 Hannan-Quinn criter. 1.019517F-statistic 237.0787 Durbin-Watson stat 1.437555Prob(F-statistic) 0.000000
catatan : Fixed Effect Terlampir
Model Random (REM)
Estimasi data panel dengan model Fixed Effect melalui variabel dummy
menunjukkan ketidakpastian model yang digunakan. Untuk mengatasi masalah ini
kita bisa menggunakan variabel residual yang dikenal dengan metode Random Effect.
Di dalam model ini kita akan memilih estimasi data panel dimana residual mungkin
saling berhubungan antar waktu dan antar individu. Sehingga model Random Effect
mengasumsikan bahwa setiap individu mempunyai perbedaan intersep yang
merupakan variabel random atau stokastik.
Persamaan Umum : Y ¿=α+ β' X ¿+μi+ε¿
Output :
Date: 11/22/12 Time: 15:08
Sample: 1998 2008
Included observations: 11
Cross-sections included: 609
Total pool (balanced) observations: 6699
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 5.040776 0.146339 34.44592 0.0000
LNL_30? 0.316948 0.035106 9.028410 0.0000
LNK_30? -0.012714 0.009245 -1.375315 0.1691
LNM_30? 0.597252 0.005496 108.6784 0.0000
LNK_30?*LNL_30? 0.001472 0.002265 0.649808 0.5158
Effects SpecificationS.D. Rho
Cross-section random 0.408319 0.5816Idiosyncratic random 0.346352 0.4184
Weighted Statistics
R-squared 0.713664 Mean dependent var 3.605993Adjusted R-squared 0.713493 S.D. dependent var 0.667818S.E. of regression 0.357459 Sum squared resid 855.3384F-statistic 4171.031 Durbin-Watson stat 1.236149Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.855214 Mean dependent var 14.55332Sum squared resid 2625.732 Durbin-Watson stat 0.402679
Catatan : Random Effect Terlampir
3. Pemilihan Model Terbaik
Uji Chow
Uji Signifikansi Fixed effect dilakukan dengan uji F statistik. Uji F ini digunakan
untuk mengetahui apakah teknik regresi data panel dengan Fixed Effect lebih baik
dari model regresi data panel tanpa variabel dummy (Common Effect) dengan melihat
Residual Sum of Squares (RSS).
Hipotesis :
Ho : Pooled Model lebih baik
H1 : Fixed Effect Model Leboh baik
Atau
Ho : α 1=α 2=……α i
H1 : minimal ada satu intersept yang berbeda.
Output :
Redundant Fixed Effects Tests
Pool: PANEL30
Test cross-section fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 22.369882 (608,6086) 0.0000
Cross-section Chi-square
7864.37000
8 608 0.0000
Cross-section fixed effects test equation:
Dependent Variable: LNY_30?
Method: Panel Least Squares
Date: 11/22/12 Time: 15:07
Sample: 1998 2008
Included observations: 11
Cross-sections included: 609
Total pool (balanced) observations: 6699
Variable Coefficient Std. Error t-Statistic Prob.
C 3.923850 0.179383 21.87414 0.0000
LNL_30? 0.336357 0.043808 7.677954 0.0000
LNK_30? 0.035424 0.012361 2.865730 0.0042
LNM_30? 0.616664 0.004774 129.1794 0.0000
LNK_30?*LNL_30? 0.004378 0.002972 1.473101 0.1408
R-squared 0.869777 Mean dependent var 14.55332
Adjusted R-squared 0.869699 S.D. dependent var 1.645467
S.E. of regression 0.593968 Akaike info criterion 1.796763
Sum squared resid 2361.629 Schwarz criterion 1.801846
Log likelihood -6013.259 Hannan-Quinn criter. 1.798519
F-statistic 11177.52 Durbin-Watson stat 0.531390
Prob(F-statistic) 0.000000
Statistik Uji : F = (RSS1−RSS2)/(N−1)(RSS2) /(NT−N−k )
Dimana : RSS1 = Sum Square Resid pooled model, RSS2 = Sum Square Resid
FEM, N=banyaknya jumlah cross section, k = banyaknya variabel.
Tolak Ho jika F hitung > F tabel atau Prob < α
Keputusan : Karena Prob < α (α=0,05) 0,00 < 0,05 maka Tolak Ho
Kesimpulan : Dengan tingkat kepercayaan 95% dapat disimpulkan bahwa Fixed
Effect Model lebih baik digunakan untuk data dibanding Pooled Model.
Berdasarkan hasil output eviews yang dihasilkan dapat dilihat bahwa probabilitas
uji F nya 0,00 dan lebih kecil dari alpha = 5% sehingga dapat disimpulkan bahwa
model fixed effect lebih baik digunakan daripada model pooled.
Uji Hausman (Fixed vs Random)
Untuk melihat apakah model mengikuti random effect atau fixed
effect.
Hipotesis :
Ho : Random effect (individual effect uncorelated) lebih baik
H1 : Fixed effect lebih baik
Atau
Ho : Cov (v i , x¿¿=0
H1 : Cov (v i , x¿¿≠0
Output :
Correlated Random Effects - Hausman Test
Pool: PANEL30
Test cross-section random effects
Test Summary
Chi-Sq.
Statistic Chi-Sq. d.f. Prob.
Cross-section random 440.226889 4 0.0000
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
LNL_30? 0.248196 0.316948 0.000129 0.0000
LNK_30? -0.011129 -0.012714 0.000004 0.4294
LNM_30? 0.575883 0.597252 0.000008 0.0000
(LNK_30?
*LNL_30?) -0.000710 0.001472 0.000000 0.0000
Cross-section random effects test equation:
Dependent Variable: LNY_30?
Method: Panel Least Squares
Date: 11/22/12 Time: 15:09
Sample: 1998 2008
Included observations: 11
Cross-sections included: 609
Total pool (balanced) observations: 6699
Variable Coefficient Std. Error t-Statistic Prob.
C 5.700524 0.155975 36.54767 0.0000
LNL_30? 0.248196 0.036903 6.725620 0.0000
LNK_30? -0.011129 0.009460 -1.176420 0.2395
LNM_30? 0.575883 0.006194 92.96837 0.0000
LNK_30?*LNL_30? -0.000710 0.002324 -0.305684 0.7599
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.959743 Mean dependent var 14.55332
Adjusted R-squared 0.955695 S.D. dependent var 1.645467
S.E. of regression 0.346352 Akaike info criterion 0.804321
Sum squared resid 730.0735 Schwarz criterion 1.427452
Log likelihood -2081.074 Hannan-Quinn criter. 1.019517
F-statistic 237.0787 Durbin-Watson stat 1.437555
Prob(F-statistic) 0.000000
Statistik Uji :X2hitung= (b−β )' Var ¿
Dimana : b= koefisien random effect; â=koefisien fixed effect
Tolak Ho jika X2hitung>X 2
k ,α (k=jumlah koef slope) atau p-value < α
Keputusan : Karena Prob<α (α=0,05) 0,00<0,05 maka Tolak Ho
Kesimpulan : Dengan tingkat kepercayaan 95% dapat disimpulkan bahwa Fixed
Effect Model lebih baik digunakan untuk data dibanding Random Effect Model.
Berdasarkan hasil output dapat dilihat bahwa probabilitas F statistiknya 0,00 atau
kurang dari alpha 5% sehingga diperoleh model terbaik yang digunakan untuk data
ini adalah FIXED EFFECT MODEL (FEM).
4. Pemilihan Estimator
Uji LM (Heteroskedastis vs Homoskedastis)
Hipotesis :
Ho : σ 2i=σ2
H1 : σ 2i ≠ σ2
Output :
LM test for hetero versus homo
chi-sqr(608) = 3348.500p-value = 0.000000
Statistik Uji :
LM = T2∑i=1
n
(σ i
2
σ 2 −1)2
X (n−1, α )
dimana T=jml observasi, n=jml individu; σ i2=varian residual persamaan ke-i; σ 2
=varian residual persamaan system.
Keputusan Tolah Ho, jika LM test > X (n−1 ,α ) atau Prob < α
Keputusan : Tolak H karena Prob < α 0,0000 < 0,05
Kesimpulan : Dengan tingkat kepercayaan 95% dapat disimpullan bahwa struktur
varian covarian residual bersifat heteroskedastik lebih baik dibandingkan dengan
yang bersifat homoskedastik
5. Interpretasi Model Terbaik :
Jadi berdasarkan uji-uji yang telah dilakukan, diperoleh model estimasi terbaik adalah
Fixed Effect Model dangan Cross-Section Weight. Adapun outputnya dapat dilihat
pada tabel di bawah ini :
Dependent Variable: LNY_30?Method: Pooled EGLS (Cross-section weights)Date: 11/23/12 Time: 12:25Sample: 1998 2008Included observations: 11Cross-sections included: 609Total pool (balanced) observations: 6699Linear estimation after one-step weighting matrix
Variable Coefficient Std. Error t-Statistic Prob.
C 4.983707 0.099668 50.00324 0.0000LNL_30? 0.182928 0.022731 8.047643 0.0000LNK_30? -0.009014 0.005790 -1.556819 0.1196LNM_30? 0.641990 0.004995 128.5368 0.0000
LNK_30?*LNL_30? -0.000327 0.001459 -0.224289 0.8225
Effects Specification
Cross-section fixed (dummy variables)
Weighted Statistics
R-squared 0.984378 Mean dependent var 22.41908Adjusted R-squared 0.982807 S.D. dependent var 11.83751S.E. of regression 0.340977 Sum squared resid 707.5897F-statistic 626.6249 Durbin-Watson stat 1.349710Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.958943 Mean dependent var 14.55332Sum squared resid 744.5808 Durbin-Watson stat 1.438758
Fixed Effect : Terlampir
Berdasarkan output di atas probabilitas F nya 0,00 lebih kecil dari alpha 5%,
sehingga dapat disimpulkan secara simultan, variabel-variabel yaitu pertumbuhan
modal, pertumbuhan tenaga kerja dan pertumbuhan bahan baku berpengaruh
signifikan terhadap pertumbuhan nilai produksi.
Adjusted R-squarednya bernilai 0,98. Artinya, perubahan variabel pertumbuhan
nilai produksi dapat dijelaskan sebesar 98% oleh variabel independentnya yaitu
pertumbuhan modal, pertumbuhan tenaga kerja dan pertumbuhan bahan baku.
Sedangkan 2% dijelaskan oleh variabel lain. Nilai Durbin Watson mendekati 2 yaitu
1,349 sehingga dapat dikatakan bahwa tidak terjadi autokorelasi.
Untuk tingkat signifikansi 95%, variabel pertumbuhan tenaga kerja dan
pertumbuhan bahan baku berpengaruh signifikan terhadap variabel pertumbuhan nilai
produksi. Sedangkan variabel pertumbuhan capital tidak berpengaruh secara
signifikan terhadap pertumbuhan nilai produksi.
Berdasarkan output juga dapat dilihat bahwa pertumbuhan tenaga kerja sebanyak
1% dapat menaikkan pertumbuhan nilai produksi sebesar 0,18%. Setiap kenaikan 1%
pada pertumbuhan capital dapat menyebabkan penurunan pertumbuhan nilai produksi
sebesar 0,009%. Dan kenaikan pertumbuhan bahan baku sebesar 1% dapat
menyebabkan kenaikan pertumbuhan nilai produksi sebesar 0.64%.
Lampiran :
Random Effect
_1--C_2--C_3--C_4--C_5--C_6--C_7--C_8--C_9--C
_10--C_11--C_12--C_13--C_14--C_15--C_16--C_17--C_18--C_19--C_20--C_21--C_22--C_23--C_24--C_25--C_26--C_27--C_28--C_29--C_30--C_31--C_32--C_33--C_34--C_35--C_36--C_37--C_38--C_39--C_40--C_41--C_42--C_43--C_44--C_45--C_46--C_47--C
-0.1841640.056264
-0.244613-0.1201530.044157
-0.1737150.743540
-0.1676950.344795
-0.0536770.3240380.1066370.7104630.9383710.0787490.0507130.9054530.6432110.3563840.6492701.1712640.4613600.4474390.4473300.3303930.4803670.5323420.3977590.071642
-0.116818-0.087054-0.102039-0.248820-0.120554-0.110880-0.115877-0.1579260.335928
-0.2217690.1900570.221738
-0.2640270.223545
-0.3635680.066870
-0.337466-0.203057
_204--C_205--C_206--C_207--C_208--C_209--C_210--C_211--C_212--C_213--C_214--C_215--C_216--C_217--C_218--C_219--C_220--C_221--C_222--C_223--C_224--C_225--C_226--C_227--C_228--C_229--C_230--C_231--C_232--C_233--C_234--C_235--C_236--C_237--C_238--C_239--C_240--C_241--C_242--C_243--C_244--C_245--C_246--C_247--C_248--C_249--C_250--C
1.5579961.110788
-0.077363-0.170898-0.250871-0.2586660.068738
-0.125148-0.3102630.3659090.1271110.378453
-0.677130-0.360533-0.281347-0.392143-0.475579-0.377696-0.454246-0.057038-0.180709-0.261408-0.228611-0.189643-0.151347-0.277730-0.079951-0.083907-0.122808-0.255302-0.735563-0.291777-0.225223-0.4234860.0701961.532391
-0.334930-0.359718-0.323838-0.3630080.1725040.250925
-0.2252980.052596
-0.1375450.708346
-0.421393
_407--C_408--C_409--C_410--C_411--C_412--C_413--C_414--C_415--C_416--C_417--C_418--C_419--C_420--C_421--C_422--C_423--C_424--C_425--C_426--C_427--C_428--C_429--C_430--C_431--C_432--C_433--C_434--C_435--C_436--C_437--C_438--C_439--C_440--C_441--C_442--C_443--C_444--C_445--C_446--C_447--C_448--C_449--C_450--C_451--C_452--C_453--C
0.0508540.3120850.203026
-0.1619830.157799
-0.522037-0.013894-0.489142-0.021984-0.1791100.644385
-0.3233470.228211
-0.2943730.5578840.107561
-0.3611890.113011
-0.621699-0.480737-0.393616-0.457961-0.2896501.0673600.241062
-0.0902270.109453
-0.0675862.2695000.5136830.4184080.808294
-0.0257170.0492100.0271131.0546360.4913140.869275
-0.1293300.057795
-0.1129360.115493
-0.098917-0.7264890.589618
-0.007466-0.185343
_48--C_49--C_50--C_51--C_52--C_53--C_54--C_55--C_56--C_57--C_58--C_59--C_60--C_61--C_62--C_63--C_64--C_65--C_66--C_67--C_68--C_69--C_70--C_71--C_72--C_73--C_74--C_75--C_76--C_77--C_78--C_79--C_80--C_81--C_82--C_83--C_84--C_85--C_86--C_87--C_88--C_89--C_90--C_91--C_92--C_93--C_94--C_95--C_96--C_97--C_98--C_99--C
_100--C_101--C_102--C
0.5200530.0458570.1377830.4060970.8507761.5309061.0069291.0577861.2723850.116794
-0.316295-0.183156-0.070334-0.353670-0.3409880.252454
-0.286714-0.2732420.8362430.016042
-0.4385890.0187740.6068900.981866
-0.0775151.551249
-0.209099-0.0789880.7710590.7280810.5779570.8843050.9295010.4135840.243008
-0.5741710.841724
-0.149097-0.1968310.173190
-0.2373950.038417
-0.292455-0.069387-0.0320570.5924370.372287
-0.254848-0.314669-0.265032-0.344630-0.285689-0.364670-0.126597-0.058029
_251--C_252--C_253--C_254--C_255--C_256--C_257--C_258--C_259--C_260--C_261--C_262--C_263--C_264--C_265--C_266--C_267--C_268--C_269--C_270--C_271--C_272--C_273--C_274--C_275--C_276--C_277--C_278--C_279--C_280--C_281--C_282--C_283--C_284--C_285--C_286--C_287--C_288--C_289--C_290--C_291--C_292--C_293--C_294--C_295--C_296--C_297--C_298--C_299--C_300--C_301--C_302--C_303--C_304--C_305--C
-0.058375-0.276907-0.2633580.708651
-0.3932970.4569700.491042
-0.569420-0.272286-0.199800-0.167673-0.7101230.2334750.1151910.0780150.229675
-0.192744-0.0838580.496039
-0.1366870.117734
-0.358251-0.0007370.2601810.161280
-0.1864700.1212930.1609190.0254940.1320250.2135790.2383220.220604
-0.2463140.0646400.217445
-0.1192910.1868070.0159730.081874
-0.5176320.0395750.226176
-0.2189690.5236660.222494
-0.2198380.0242460.113683
-0.037639-0.457048-0.0040350.2747970.1436440.189159
_454--C_455--C_456--C_457--C_458--C_459--C_460--C_461--C_462--C_463--C_464--C_465--C_466--C_467--C_468--C_469--C_470--C_471--C_472--C_473--C_474--C_475--C_476--C_477--C_478--C_479--C_480--C_481--C_482--C_483--C_484--C_485--C_486--C_487--C_488--C_489--C_490--C_491--C_492--C_493--C_494--C_495--C_496--C_497--C_498--C_499--C_500--C_501--C_502--C_503--C_504--C_505--C_506--C_507--C_508--C
0.982675-0.0490880.016760
-0.093884-0.013827-0.080389-0.075825-0.200917-0.375585-0.538817-0.562132-0.437434-0.397688-0.197564-0.232746-0.343604-0.2351290.737328
-0.155370-0.1274930.105637
-0.389343-0.319170-0.175429-0.4113651.097432
-0.2280850.003323
-0.235683-0.018306-0.2993040.186321
-0.072673-0.101661-0.0295050.0183030.025975
-0.2292900.144701
-0.0447750.1298630.137271
-0.534153-0.5051280.601878
-0.106452-0.125325-0.103145-0.133790-0.1248640.226633
-0.3766690.762692
-0.277610-0.386411
_103--C_104--C_105--C_106--C_107--C_108--C_109--C_110--C_111--C_112--C_113--C_114--C_115--C_116--C_117--C_118--C_119--C_120--C_121--C_122--C_123--C_124--C_125--C_126--C_127--C_128--C_129--C_130--C_131--C_132--C_133--C_134--C_135--C_136--C_137--C_138--C_139--C_140--C_141--C_142--C_143--C_144--C_145--C_146--C_147--C_148--C_149--C_150--C_151--C_152--C_153--C_154--C_155--C_156--C_157--C
-0.1872020.587270
-0.454896-0.360499-0.2300390.7143450.081127
-0.097750-0.1258740.153691
-0.284679-0.012651-0.648206-0.780947-0.115768-0.730314-0.211855-0.347342-0.4964690.0696240.4920670.1463100.0054950.7966021.9255290.6237032.313116
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-0.437243-0.2990110.0784610.112699
-0.4161930.390005
-0.2960170.195219
_306--C_307--C_308--C_309--C_310--C_311--C_312--C_313--C_314--C_315--C_316--C_317--C_318--C_319--C_320--C_321--C_322--C_323--C_324--C_325--C_326--C_327--C_328--C_329--C_330--C_331--C_332--C_333--C_334--C_335--C_336--C_337--C_338--C_339--C_340--C_341--C_342--C_343--C_344--C_345--C_346--C_347--C_348--C_349--C_350--C_351--C_352--C_353--C_354--C_355--C_356--C_357--C_358--C_359--C_360--C
0.0045250.347008
-0.1614380.230073
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-0.208876-0.113277-0.137344-0.266693-0.127376-0.437191-0.369461
_509--C_510--C_511--C_512--C_513--C_514--C_515--C_516--C_517--C_518--C_519--C_520--C_521--C_522--C_523--C_524--C_525--C_526--C_527--C_528--C_529--C_530--C_531--C_532--C_533--C_534--C_535--C_536--C_537--C_538--C_539--C_540--C_541--C_542--C_543--C_544--C_545--C_546--C_547--C_548--C_549--C_550--C_551--C_552--C_553--C_554--C_555--C_556--C_557--C_558--C_559--C_560--C_561--C_562--C_563--C
-0.455206-0.627258-0.585207-0.363991-0.202453-0.9463490.563932
-0.3252420.451449
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-0.049416-0.2786911.0488561.007695
-0.704462-0.001927-0.259003-0.6064100.014101
_158--C_159--C_160--C_161--C_162--C_163--C_164--C_165--C_166--C_167--C_168--C_169--C_170--C_171--C_172--C_173--C_174--C_175--C_176--C_177--C_178--C_179--C_180--C_181--C_182--C_183--C_184--C_185--C_186--C_187--C_188--C_189--C_190--C_191--C_192--C_193--C_194--C_195--C_196--C_197--C_198--C_199--C_200--C_201--C_202--C_203--C
-0.175767-0.316465-0.581744-0.214002-0.530728-0.509815-0.186331-0.490890-0.478385-0.312340-0.616793-0.471776-0.266620-0.308559-0.286764-0.4579210.634279
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_361--C_362--C_363--C_364--C_365--C_366--C_367--C_368--C_369--C_370--C_371--C_372--C_373--C_374--C_375--C_376--C_377--C_378--C_379--C_380--C_381--C_382--C_383--C_384--C_385--C_386--C_387--C_388--C_389--C_390--C_391--C_392--C_393--C_394--C_395--C_396--C_397--C_398--C_399--C_400--C_401--C_402--C_403--C_404--C_405--C_406--C
-0.057196-0.034251-0.048517-0.192568-0.455791-0.123086-0.301734-0.1534390.072543
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_564--C_565--C_566--C_567--C_568--C_569--C_570--C_571--C_572--C_573--C_574--C_575--C_576--C_577--C_578--C_579--C_580--C_581--C_582--C_583--C_584--C_585--C_586--C_587--C_588--C_589--C_590--C_591--C_592--C_593--C_594--C_595--C_596--C_597--C_598--C_599--C_600--C_601--C_602--C_603--C_604--C_605--C_606--C_607--C_608--C_609--C
0.300526-0.400420-0.433163-0.3994980.2461580.937082
-0.066143-0.0051650.7393790.8403710.3224121.487619
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Fixed effect
_1--C_2--C_3--C_4--C_5--C_6--C_7--C_8--C_9--C
_10--C_11--C_12--C_13--C_14--C_15--C_16--C_17--C_18--C_19--C_20--C_21--C_22--C_23--C_24--C_25--C_26--C_27--C_28--C_29--C_30--C_31--C_32--C_33--C_34--C_35--C_36--C_37--C_38--C_39--C_40--C_41--C_42--C_43--C_44--C_45--C_46--C_47--C
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-0.448494-0.333006
_204--C_205--C_206--C_207--C_208--C_209--C_210--C_211--C_212--C_213--C_214--C_215--C_216--C_217--C_218--C_219--C_220--C_221--C_222--C_223--C_224--C_225--C_226--C_227--C_228--C_229--C_230--C_231--C_232--C_233--C_234--C_235--C_236--C_237--C_238--C_239--C_240--C_241--C_242--C_243--C_244--C_245--C_246--C_247--C_248--C_249--C_250--C
1.5429031.092581
-0.047497-0.140827-0.268774-0.3451340.0546480.071146
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-0.2630870.0343020.0923971.038470
-0.512059
_407--C_408--C_409--C_410--C_411--C_412--C_413--C_414--C_415--C_416--C_417--C_418--C_419--C_420--C_421--C_422--C_423--C_424--C_425--C_426--C_427--C_428--C_429--C_430--C_431--C_432--C_433--C_434--C_435--C_436--C_437--C_438--C_439--C_440--C_441--C_442--C_443--C_444--C_445--C_446--C_447--C_448--C_449--C_450--C_451--C_452--C_453--C
0.2233730.2908790.563650
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-0.231627
_48--C_49--C_50--C_51--C_52--C_53--C_54--C_55--C_56--C_57--C_58--C_59--C_60--C_61--C_62--C_63--C_64--C_65--C_66--C_67--C_68--C_69--C_70--C_71--C_72--C_73--C_74--C_75--C_76--C_77--C_78--C_79--C_80--C_81--C_82--C_83--C_84--C_85--C_86--C_87--C_88--C_89--C_90--C_91--C_92--C_93--C_94--C_95--C_96--C_97--C_98--C_99--C
_100--C_101--C_102--C
0.5669080.0655820.1153780.4802760.8168921.6608040.9556481.0627211.2921170.272561
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_251--C_252--C_253--C_254--C_255--C_256--C_257--C_258--C_259--C_260--C_261--C_262--C_263--C_264--C_265--C_266--C_267--C_268--C_269--C_270--C_271--C_272--C_273--C_274--C_275--C_276--C_277--C_278--C_279--C_280--C_281--C_282--C_283--C_284--C_285--C_286--C_287--C_288--C_289--C_290--C_291--C_292--C_293--C_294--C_295--C_296--C_297--C_298--C_299--C_300--C_301--C_302--C_303--C_304--C_305--C
0.122633-0.162132-0.1060141.150769
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-0.718602-0.324696-0.303816-0.271877-0.9080910.6376130.0762620.0490940.183531
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_454--C_455--C_456--C_457--C_458--C_459--C_460--C_461--C_462--C_463--C_464--C_465--C_466--C_467--C_468--C_469--C_470--C_471--C_472--C_473--C_474--C_475--C_476--C_477--C_478--C_479--C_480--C_481--C_482--C_483--C_484--C_485--C_486--C_487--C_488--C_489--C_490--C_491--C_492--C_493--C_494--C_495--C_496--C_497--C_498--C_499--C_500--C_501--C_502--C_503--C_504--C_505--C_506--C_507--C_508--C
1.385036-0.1087640.191011
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-0.4909610.993044
-0.405268-0.457717
_103--C_104--C_105--C_106--C_107--C_108--C_109--C_110--C_111--C_112--C_113--C_114--C_115--C_116--C_117--C_118--C_119--C_120--C_121--C_122--C_123--C_124--C_125--C_126--C_127--C_128--C_129--C_130--C_131--C_132--C_133--C_134--C_135--C_136--C_137--C_138--C_139--C_140--C_141--C_142--C_143--C_144--C_145--C_146--C_147--C_148--C_149--C_150--C_151--C_152--C_153--C_154--C_155--C_156--C_157--C
-0.2872540.790672
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_306--C_307--C_308--C_309--C_310--C_311--C_312--C_313--C_314--C_315--C_316--C_317--C_318--C_319--C_320--C_321--C_322--C_323--C_324--C_325--C_326--C_327--C_328--C_329--C_330--C_331--C_332--C_333--C_334--C_335--C_336--C_337--C_338--C_339--C_340--C_341--C_342--C_343--C_344--C_345--C_346--C_347--C_348--C_349--C_350--C_351--C_352--C_353--C_354--C_355--C_356--C_357--C_358--C_359--C_360--C
-0.0577840.359075
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-0.545883-0.782703-0.718333-0.423915-0.184618-1.1804340.946151
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_158--C_159--C_160--C_161--C_162--C_163--C_164--C_165--C_166--C_167--C_168--C_169--C_170--C_171--C_172--C_173--C_174--C_175--C_176--C_177--C_178--C_179--C_180--C_181--C_182--C_183--C_184--C_185--C_186--C_187--C_188--C_189--C_190--C_191--C_192--C_193--C_194--C_195--C_196--C_197--C_198--C_199--C_200--C_201--C_202--C_203--C
-0.256599-0.280845-0.640901-0.251607-0.600576-0.571167-0.218083-0.532753-0.529034-0.365561-0.773774-0.443684-0.315705-0.282604-0.419575-0.5429950.962802
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_361--C_362--C_363--C_364--C_365--C_366--C_367--C_368--C_369--C_370--C_371--C_372--C_373--C_374--C_375--C_376--C_377--C_378--C_379--C_380--C_381--C_382--C_383--C_384--C_385--C_386--C_387--C_388--C_389--C_390--C_391--C_392--C_393--C_394--C_395--C_396--C_397--C_398--C_399--C_400--C_401--C_402--C_403--C_404--C_405--C_406--C
-0.122810-0.053985-0.067092-0.278168-0.561351-0.154505-0.391803-0.1507210.028189
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