Kuis Ekonometrik
Nama
: Suci Safitriani 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, variabelvariabel 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 :
Output :
Group unit root test: Summary Series: LNY_30_1 Date: 11/22/12 Time: 14:50 Sample: 1998 2008 Exogenous variables: Individual effects Automatic selection of maximum lags Automatic selection of lags based on SIC: S IC: 0 to 1 Newey-West bandwidth selection using Bartlett kernel CrossMethod Statistic Prob.** sections Null: Unit root (assumes common unit root process)
Obs
Levin, Lin & Chu t*
-1504.01
0.0000 0.000 0
2422
23800
Null: Unit root (assumes individual unit root process) process) Im, Pesaran and Shin Wstat -70.0799 0.0000 2422 ADF - Fisher Chi-square 10124.2 0.0000 2422 PP - Fisher Chi-square 11619.5 0.0000 2422
23800 23800 24220
** Probabilities for Fisher tests are computed using an as ymptotic 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 :
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
F-statistic
11177.52
Prob(F-statistic)
0.000000
Hannan-Quinn criter. 1.798519 Durbin-Watson stat
0.531390
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 :
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
C LNL_30? LNK_30? LNM_30? LNK_30?*LNL_30?
5.700524 0.248196 -0.011129 0.575883 -0.000710
0.155975 0.036903 0.009460 0.006194 0.002324
36.54767 6.725620 -1.176420 92.96837 -0.305684
Prob. 0.0000 0.0000 0.2395 0.0000 0.7599
Effects Specification Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.959743 0.955695 0.346352 730.0735 -2081.074 237.0787 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
14.55332 1.645467 0.804321 1.427452 1.019517 1.437555
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 :
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 varianc es 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 Specification S.D. Cross-section random Idiosyncratic random
0.408319 0.346352
Rho 0.5816 0.4184
Weighted Statistics R-squared Adjusted R-squared S.E. of regression F-statistic Prob(F-statistic)
0.713664 0.713493 0.357459 4171.031 0.000000
Mean dependent var S.D. dependent var Sum squared resid Durbin-Watson stat
3.605993 0.667818 855.3384 1.236149
Unweighted Statistics R-squared Sum squared resid
0.855214 2625.732
Mean dependent var Durbin-Watson stat
14.55332 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 :
H1 : minimal ada satu intersept yang berbeda.
Output :
Redundant Fixed Effects Tests Pool: PANEL30 Test cross-section fixed effects Effects Test
Statistic
Cross-section F
d.f.
22.369882 (608,6086)
Prob. 0.0000
7864.37000 Cross-section Chi-square
8
608
Std. Error
t-Statistic
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
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
F-statistic
11177.52
Prob(F-statistic)
0.000000
Statistik Uji : F =
Hannan-Quinn criter. 1.798519 Durbin-Watson stat
0.531390
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 (
H1 : Cov (
Output :
Correlated Random Effects - Hausman Test Pool: PANEL30 Test cross-section random effects Chi-Sq. Test Summary
Statistic Chi-Sq. d.f.
Cross-section random
440.226889
4
Prob. 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
-0.000710
0.001472
0.000000
0.0000
(LNK_30?*LNL_30 ?)
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
F-statistic
237.0787
Prob(F-statistic)
0.000000
Hannan-Quinn criter. 1.019517 Durbin-Watson stat
1.437555
Statistik Uji : Dimana : b= koefisien random effect; â=koefisien fixed effect
Tolak Ho jika (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 :
H1 :
Output :
LM test for hetero versus homo chi-sqr(608) = p-value =
3348.500 0.000000
Statistik Uji :
LM = ∑
dimana T=jml observasi, n=jml individu; =varian residual persamaan ke-i;
=varian residual persamaan system. Keputusan Tolah Ho, jika LM test > atau Prob <
Keputusan : Tolak H karena Prob < 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, d iperoleh 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: 12:25 Sample: 1998 2008 Included observations: 11 Cross-sections included: 609 Total pool (balanced) observations: 6699 Linear estimation after one-step weighting matrix Variable
Coefficient
Std. Error
t-Statistic
C LNL_30? LNK_30? LNM_30? LNK_30?*LNL_30?
4.983707 0.182928 -0.009014 0.641990 -0.000327
0.099668 0.022731 0.005790 0.004995 0.001459
50.00324 8.047643 -1.556819 128.5368 -0.224289
Prob. 0.0000 0.0000 0.1196 0.0000 0.8225
Effects Specification Cross-section fixed (dummy variables) Weighted Statistics R-squared Adjusted R-squared S.E. of regression F-statistic Prob(F-statistic)
0.984378 0.982807 0.340977 626.6249 0.000000
Mean dependent var S.D. dependent var Sum squared resid Durbin-Watson stat
22.41908 11.83751 707.5897 1.349710
Unweighted Statistics R-squared Sum squared resid
0.958943 744.5808
Mean dependent var Durbin-Watson stat
14.55332 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
-0.184164 0.056264 -0.244613 -0.120153 0.044157 -0.173715 0.743540 -0.167695 0.344795 -0.053677 0.324038 0.106637 0.710463 0.938371 0.078749 0.050713 0.905453 0.643211 0.356384 0.649270 1.171264 0.461360 0.447439 0.447330 0.330393 0.480367 0.532342 0.397759 0.071642 -0.116818 -0.087054 -0.102039 -0.248820 -0.120554 -0.110880 -0.115877 -0.157926 0.335928 -0.221769 0.190057 0.221738 -0.264027 0.223545 -0.363568 0.066870 -0.337466
_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
1.557996 1.110788 -0.077363 -0.170898 -0.250871 -0.258666 0.068738 -0.125148 -0.310263 0.365909 0.127111 0.378453 -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 -0.423486 0.070196 1.532391 -0.334930 -0.359718 -0.323838 -0.363008 0.172504 0.250925 -0.225298 0.052596 -0.137545 0.708346
_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
0.050854 0.312085 0.203026 -0.161983 0.157799 -0.522037 -0.013894 -0.489142 -0.021984 -0.179110 0.644385 -0.323347 0.228211 -0.294373 0.557884 0.107561 -0.361189 0.113011 -0.621699 -0.480737 -0.393616 -0.457961 -0.289650 1.067360 0.241062 -0.090227 0.109453 -0.067586 2.269500 0.513683 0.418408 0.808294 -0.025717 0.049210 0.027113 1.054636 0.491314 0.869275 -0.129330 0.057795 -0.112936 0.115493 -0.098917 -0.726489 0.589618 -0.007466
_47--C _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
-0.203057 0.520053 0.045857 0.137783 0.406097 0.850776 1.530906 1.006929 1.057786 1.272385 0.116794 -0.316295 -0.183156 -0.070334 -0.353670 -0.340988 0.252454 -0.286714 -0.273242 0.836243 0.016042 -0.438589 0.018774 0.606890 0.981866 -0.077515 1.551249 -0.209099 -0.078988 0.771059 0.728081 0.577957 0.884305 0.929501 0.413584 0.243008 -0.574171 0.841724 -0.149097 -0.196831 0.173190 -0.237395 0.038417 -0.292455 -0.069387 -0.032057 0.592437 0.372287 -0.254848 -0.314669 -0.265032 -0.344630 -0.285689 -0.364670 -0.126597
_250--C _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
-0.421393 -0.058375 -0.276907 -0.263358 0.708651 -0.393297 0.456970 0.491042 -0.569420 -0.272286 -0.199800 -0.167673 -0.710123 0.233475 0.115191 0.078015 0.229675 -0.192744 -0.083858 0.496039 -0.136687 0.117734 -0.358251 -0.000737 0.260181 0.161280 -0.186470 0.121293 0.160919 0.025494 0.132025 0.213579 0.238322 0.220604 -0.246314 0.064640 0.217445 -0.119291 0.186807 0.015973 0.081874 -0.517632 0.039575 0.226176 -0.218969 0.523666 0.222494 -0.219838 0.024246 0.113683 -0.037639 -0.457048 -0.004035 0.274797 0.143644
_453--C _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
-0.185343 0.982675 -0.049088 0.016760 -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 -0.235129 0.737328 -0.155370 -0.127493 0.105637 -0.389343 -0.319170 -0.175429 -0.411365 1.097432 -0.228085 0.003323 -0.235683 -0.018306 -0.299304 0.186321 -0.072673 -0.101661 -0.029505 0.018303 0.025975 -0.229290 0.144701 -0.044775 0.129863 0.137271 -0.534153 -0.505128 0.601878 -0.106452 -0.125325 -0.103145 -0.133790 -0.124864 0.226633 -0.376669 0.762692 -0.277610
_102--C _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
-0.058029 -0.187202 0.587270 -0.454896 -0.360499 -0.230039 0.714345 0.081127 -0.097750 -0.125874 0.153691 -0.284679 -0.012651 -0.648206 -0.780947 -0.115768 -0.730314 -0.211855 -0.347342 -0.496469 0.069624 0.492067 0.146310 0.005495 0.796602 1.925529 0.623703 2.313116 -0.170136 -0.106230 -0.250086 -0.262139 -0.078832 -0.607216 -0.571977 0.015100 -0.211562 -0.018745 0.242485 -0.430345 -0.251724 -0.063782 0.820220 0.412296 1.376655 0.369572 0.370346 1.149535 -0.437243 -0.299011 0.078461 0.112699 -0.416193 0.390005 -0.296017
_305--C _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
0.189159 0.004525 0.347008 -0.161438 0.230073 -0.187088 -0.488867 -0.245919 0.035217 -0.209467 -0.402009 -0.527847 -0.386042 -0.327737 -0.643220 -0.376954 -0.559391 -0.566716 -0.343431 -0.466183 -0.599310 -0.625048 -0.235842 -0.417912 -0.332316 -0.542218 -0.140831 -0.371926 -0.236036 -0.310421 -0.573577 -0.171573 -0.323935 0.288514 -0.166992 -0.088417 0.284759 0.046710 0.190915 -0.289675 0.882860 0.584468 0.860566 0.996977 0.740267 0.757953 0.678178 1.099859 0.835531 -0.208876 -0.113277 -0.137344 -0.266693 -0.127376 -0.437191
_508--C _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
-0.386411 -0.455206 -0.627258 -0.585207 -0.363991 -0.202453 -0.946349 0.563932 -0.325242 0.451449 -0.011910 1.610988 -0.080989 -0.077345 0.517683 -0.191488 -0.267267 -0.392208 -0.393794 -0.265100 -0.057402 -0.048035 -0.174887 1.531248 0.046320 -0.468113 -0.443064 -0.396742 -0.120444 -0.544556 -0.003494 0.073311 -0.191683 -0.536601 -0.996744 -0.992883 0.046195 0.270681 -0.272191 0.002977 -0.203674 -0.228432 -0.184376 -0.259724 -0.522471 -0.527807 0.081876 -0.049416 -0.278691 1.048856 1.007695 -0.704462 -0.001927 -0.259003 -0.606410
_157--C _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
0.195219 -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 -0.457921 0.634279 -0.896356 -0.265735 -0.073518 -0.689117 -0.458869 -0.332112 0.471020 -0.559039 0.322555 -0.543100 -0.231043 0.376198 0.155301 -0.264705 0.133614 0.082098 -0.030661 -0.175459 1.238233 1.556895 0.976444 1.070099 1.345445 1.966536 0.986958 1.230879 1.200054 1.176408 0.572415
_360--C _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
-0.369461 -0.057196 -0.034251 -0.048517 -0.192568 -0.455791 -0.123086 -0.301734 -0.153439 0.072543 -0.197636 -0.057779 0.075234 -0.463832 -0.181543 -0.121351 -0.261138 -0.485531 -0.415953 -0.617321 -0.220205 -0.404660 -0.400849 -0.367342 -0.345006 -0.335272 -0.392628 -0.226619 -0.410615 -0.410244 -0.540379 -0.401752 -0.535855 -0.220115 -0.345596 -0.132312 0.245446 -0.315092 -0.350533 0.278788 -0.376882 0.074986 -0.054596 0.801793 -0.242825 -0.412471 -0.283495
_563--C _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
0.014101 0.300526 -0.400420 -0.433163 -0.399498 0.246158 0.937082 -0.066143 -0.005165 0.739379 0.840371 0.322412 1.487619 -0.412135 0.188416 -0.470584 -0.240668 -0.385653 -0.178472 -0.999186 -1.026581 -1.007610 -0.988358 -1.032861 -0.256166 -0.203485 0.338434 -0.079576 0.175150 0.304986 -0.032077 0.342196 0.014872 -0.144697 -0.374411 0.346202 0.402196 0.228103 -0.435973 -0.400100 -0.498835 -0.080871 -0.450534 -0.178623 -0.187932 -0.416375 -0.531102
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
-0.321019 -0.069825 -0.380227 -0.244172 -0.073930 -0.329471 1.133240 -0.251124 0.305934 -0.124793 0.498413 0.093048 1.022983 1.220815 0.014378 0.015799 1.321807 0.864826 0.812450 1.051533 1.536097 0.603840 0.456616 0.561337 0.296873 0.516010 0.577870 0.460207 0.016946 -0.201314 -0.166278 -0.187935 -0.353726 -0.206988 -0.199138 -0.200201 -0.249095 0.341477 -0.256095 0.123640 0.142458 -0.226018 0.644056 -0.434685 0.022808 -0.448494
_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
1.542903 1.092581 -0.047497 -0.140827 -0.268774 -0.345134 0.054648 0.071146 -0.354797 0.869521 0.324158 0.923352 -0.806292 -0.393659 -0.308264 -0.532950 -0.627456 -0.410279 -0.592327 -0.081222 -0.284636 -0.317776 -0.270196 -0.190155 -0.266391 -0.330624 -0.135298 -0.029484 -0.126296 -0.264591 -0.908487 -0.343977 -0.264571 -0.508193 0.071639 2.076636 -0.450242 -0.462802 -0.364326 -0.460926 0.312386 0.565125 -0.263087 0.034302 0.092397 1.038470
_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
0.223373 0.290879 0.563650 -0.108857 0.331073 -0.382831 -0.031502 -0.487657 0.039111 -0.225170 0.807424 -0.397230 0.220150 -0.420696 0.865024 0.117341 -0.399963 0.087539 -0.669996 -0.281768 -0.497904 -0.577502 -0.366674 1.440225 0.219859 -0.119323 0.040479 -0.109489 2.417289 0.895587 0.737610 1.137916 -0.138859 0.045552 0.217408 1.008423 0.378803 0.818904 -0.164602 0.053881 0.011209 -0.012911 -0.142261 -0.904478 0.542522 0.055391
_47--C _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
-0.333006 0.566908 0.065582 0.115378 0.480276 0.816892 1.660804 0.955648 1.062721 1.292117 0.272561 -0.408200 -0.214271 -0.110381 -0.465313 -0.426307 0.318811 -0.374352 -0.398804 0.897497 -0.008923 -0.587532 -0.062254 0.793686 1.330356 -0.052892 1.663966 -0.336869 -0.151819 0.824739 0.789181 0.691484 1.210333 1.217523 0.400766 0.218514 -0.715000 1.052518 -0.195068 -0.160413 0.394970 -0.380525 -0.063285 -0.400290 -0.065191 -0.153424 0.543927 0.539820 -0.371852 -0.454313 -0.303633 -0.280030 -0.402923 -0.463344 -0.197233
_250--C _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
-0.512059 0.122633 -0.162132 -0.106014 1.150769 -0.208362 0.889570 0.682618 -0.718602 -0.324696 -0.303816 -0.271877 -0.908091 0.637613 0.076262 0.049094 0.183531 -0.198645 -0.146321 0.566092 -0.202359 0.128244 -0.403691 -0.064071 0.292995 0.193399 -0.268203 0.091792 0.287750 -0.022662 0.106712 0.252029 0.318276 0.323729 -0.372924 0.182567 0.301930 -0.165326 0.160558 -0.058882 0.044693 -0.621034 0.214493 0.196217 -0.315493 0.493248 0.212957 -0.321143 -0.013864 0.101396 0.131302 -0.520898 -0.071827 0.318533 0.186032
_453--C _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
-0.231627 1.385036 -0.108764 0.191011 -0.152511 -0.079341 -0.143382 -0.147887 -0.117776 -0.171282 -0.624353 -0.539374 -0.544738 -0.480552 -0.009492 -0.326818 -0.420674 -0.387789 1.017606 -0.252562 -0.227629 0.033517 -0.527489 -0.428875 -0.279996 -0.565048 1.576576 -0.323312 0.052057 -0.292536 -0.046625 -0.390644 0.107092 -0.097618 -0.147740 -0.057920 -0.012758 -0.036954 -0.323752 0.123002 -0.087212 0.111043 0.180977 -0.667418 -0.633105 0.844351 -0.175943 -0.185987 -0.165224 -0.208007 0.021313 0.322886 -0.490961 0.993044 -0.405268
_102--C _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
-0.013251 -0.287254 0.790672 -0.542940 -0.458249 -0.338987 0.942826 0.042462 -0.192269 -0.215819 0.160948 -0.292791 0.038255 -0.721815 -0.949493 -0.146875 -0.840391 -0.266590 -0.424507 -0.647959 0.008946 0.635555 0.125758 -0.075972 1.151928 1.965466 0.527525 2.368310 -0.212964 -0.188465 -0.256452 -0.225761 -0.109818 -0.767790 -0.727548 0.085849 -0.317935 -0.144103 0.472129 -0.555601 -0.184410 -0.147022 1.044522 0.552311 1.797331 0.820233 0.402603 1.279318 -0.488675 -0.314332 0.113780 0.179809 -0.523339 0.740719 -0.337849
_305--C _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
0.175362 -0.057784 0.359075 -0.256159 0.446149 0.070633 -0.563880 -0.240185 0.154830 -0.331411 -0.473050 -0.596408 -0.509256 -0.287333 -0.818826 -0.351848 -0.527948 -0.628656 -0.241302 -0.527651 -0.678151 -0.771495 -0.315524 -0.447117 -0.247564 -0.681317 -0.099102 -0.451473 -0.274116 -0.360865 -0.712932 -0.272350 -0.260374 0.493734 -0.229696 0.052426 0.335675 0.494202 0.278824 -0.265781 0.825551 0.497183 0.777238 1.034338 0.709436 0.700129 0.691854 1.004669 0.719845 -0.317033 -0.231175 -0.093781 -0.357187 -0.217538 -0.582129
_508--C _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
-0.457717 -0.545883 -0.782703 -0.718333 -0.423915 -0.184618 -1.180434 0.946151 -0.412774 0.636631 -0.049816 1.603613 -0.128037 -0.234226 0.906946 -0.289887 -0.209846 -0.539542 -0.536138 -0.318288 0.068899 0.013128 -0.237797 1.561370 0.042307 -0.585742 -0.574262 -0.458496 -0.106690 -0.721658 -0.064572 0.022881 -0.234013 -0.541489 -1.202412 -1.197665 0.084763 0.250137 -0.108699 -0.039448 -0.269625 -0.324031 -0.286625 -0.289658 -0.491350 -0.646060 0.111374 -0.141702 -0.400138 0.984958 1.009027 -0.864429 -0.122454 -0.343504 -0.745063
_157--C _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
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