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. encode kode, gen(id)
. xtset id tahun
panel variable: id (strongly balanced)
time variable: tahun, 1999 to 2001
delta: 1 unit
1. Pooled Least Square (PLS)
2. Fixed Effect / Least Square Dummy Variable
3. Random Effect / Error Component Model
Which one better?
Test!
PENDEKATAN PANEL
. xtreg abs_dac - ta, fe
Fixed-effects (within) regression Number of obs = 309
Group variable: id Number of groups = 103
R-sq: within = 0.0363 Obs per group: min = 3
between = 0.1214 avg = 3.0
overall = 0.0160 max = 3
F(7,199) = 1.07
corr(u_i, Xb) = -0.4436 Prob > F = 0.3829
------------------------------------------------------------------------------
abs_dac | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spesialisasi | .042161 .0241284 1.75 0.082 -.0054192 .0897413
big4 | -.0376224 .0413772 -0.91 0.364 -.1192165 .0439717
lev | .0091587 .0245977 0.37 0.710 -.0393468 .0576642
growth | .0069892 .0055505 1.26 0.209 -.0039561 .0179345
sizelnta | .0009292 .0211456 0.04 0.965 -.0407691 .0426275
d_loss | -.0259335 .0217378 -1.19 0.234 -.0687995 .0169325
ta | 9.11e -10 2.49e-09 0.37 0.715 -4.01e-09 5.83e-09
_cons | .0837001 .4287693 0.20 0.845 -.7618143 .9292144
-------------+----------------------------------------------------------------
sigma_u | .09620508
sigma_e | .10184037
rho | .47156848 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(102, 199) = 1.62 Prob > F = 0.0019
PLS VS FIXED EFFECT: CHOW TEST
H0: PLS
H1: FE
Tolak h0 jika Prob > F kurang dari alfa
Hasil: tolak H0 FE
CHOW TEST
1. . xtreg abs_dac - ta, fe
2. . estimates store fe
3. . xtreg abs_dac – ta
4. . estimates store re
5. . hausman fe re
FIXED VS RANDOM: HASUMAN TEST
. xtreg abs_dac - ta, fe
Fixed-effects (within) regression Number of obs = 309
Group variable: id Number of groups = 103
R-sq: within = 0.0363 Obs per group: min = 3
between = 0.1214 avg = 3.0
overall = 0.0160 max = 3
F(7,199) = 1.07
corr(u_i, Xb) = -0.4436 Prob > F = 0.3829
------------------------------------------------------------------------------
abs_dac | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spesialisasi | .042161 .0241284 1.75 0.082 -.0054192 .0897413
big4 | -.0376224 .0413772 -0.91 0.364 -.1192165 .0439717
lev | .0091587 .0245977 0.37 0.710 -.0393468 .0576642
growth | .0069892 .0055505 1.26 0.209 -.0039561 .0179345
sizelnta | .0009292 .0211456 0.04 0.965 -.0407691 .0426275
d_loss | -.0259335 .0217378 -1.19 0.234 -.0687995 .0169325
ta | 9.11e-10 2.49e-09 0.37 0.715 -4.01e-09 5.83e-09
_cons | .0837001 .4287693 0.20 0.845 -.7618143 .9292144
-------------+----------------------------------------------------------------
sigma_u | .09620508
sigma_e | .10184037
rho | .47156848 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(102, 199) = 1.62 Prob > F = 0.0019
. estimates store fe
HASUMAN TEST
. xtreg abs_dac - ta
Random-effects GLS regression Number of obs = 309
Group variable: id Number of groups = 103
R-sq: within = 0.0002 Obs per group: min = 3
between = 0.3167 avg = 3.0
overall = 0.1413 max = 3
Random effects u_i ~ Gaussian Wald chi2(7) = 35.95
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
abs_dac | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spesialisasi | -.0119793 .0153625 -0.78 0.436 -.0420893 .0181306
big4 | -.0153878 .02211 -0.70 0.486 -.0587226 .0279471
lev | .0626196 .0137332 4.56 0.000 .0357031 .0895361
growth | -.0034438 .0038282 -0.90 0.368 -.010947 .0040594
sizelnta | -.0160249 .0067692 -2.37 0.018 -.0292922 -.0027576
d_loss | -.0056008 .016712 -0.34 0.738 -.0383557 .0271542
ta | 1.32e-09 1.69e-09 0.78 0.436 -2.00e-09 4.63e-09
_cons | .4081115 .1325684 3.08 0.002 .1482822 .6679408
-------------+----------------------------------------------------------------
sigma_u | .0416745
sigma_e | .10184037
rho | .14343668 (fraction of variance due to u_i)
------------------------------------------------------------------------------
. estimates store re
HASUMAN TEST
. hausman fe re
Note: the rank of the differenced variance matrix (6) does not equal the number of
coefficients being tested (7); be sure this is what you expect, or there may be
problems computing the test. Examine the output of your estimators for anything
unexpected and possibly consider scaling your variables so that the coefficients
are on a similar scale.
---- Coefficients ----
| (b) (B) (b -B) sqrt(diag(V_b-V_B))
| fe re Difference S.E.
-------------+----------------------------------------------------------------
spesialisasi | .042161 -.0119793 .0541404 .0186058
big4 | -.0376224 -.0153878 -.0222346 .0349746
lev | .0091587 .0626196 -.0534608 .020407
growth | .0069892 -.0034438 .010433 .004019
sizelnta | .0009292 -.0160249 .0169541 .0200329
d_loss | -.0259335 -.0056008 -.0203327 .0139011
ta | 9.11e-10 1.32e-09 -4.07e-10 1.83e-09
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 20.92
Prob>chi2 = 0.0019
Hasil: tolak h0 FE
Per lu kah? T idak
. xttest0
Breusch and Pagan Lagrangian multiplier test for random effects
abs_dac[id,t] = Xb + u[id] + e[id,t]
Estimated results:
| Var sd = sqrt(Var)
---------+-----------------------------
abs_dac | .014316 .1196494
e | .0103715 .1018404
u | .0017368 .0416745
Test: Var(u) = 0
chi2(1) = 3.71
Prob > chi2 = 0.0540
H0 : PLS
H1: FE Hasi l : t idak to lak h0 PLS
RANDOM VS PLS: LM TEST
. xttest3
Modified Wald test for groupwise
heteroskedasticity
in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i homo
chi2 (103) = 2.4e+05
Prob>chi2 = 0.0000
Hasil: tolak h0 ada hetero
PENGUJIAN BLUE - HETERO
. corr spesialisasi- ta
(obs=309)
| spesia~i big4 lev growth sizelnta d_loss ta
-------------+------------------------------------------------------------
spesialisasi | 1.0000
big4 | 0.3894 1.0000
lev | 0.0271 -0.0533 1.0000
growth | -0.0211 0.0359 -0.2325 1.0000
sizelnta | 0.2395 0.1254 0.0742 0.0539 1.0000
d_loss | -0.0198 -0.0245 0.4749 -0.1857 0.0480 1.0000
ta | 0.1704 -0.0454 0.0079 0.0098 0.6190 -0.0233 1.00
PENGUJIAN BLUE - MULTIKOLINEARITAS
. vif, uncentered
Variable | VIF 1/VIF
-------------+----------------------
sizelnta | 11.30 0.088476 multikol
big4 | 8.83 0.113208
lev | 3.59 0.278708
spesialisasi | 3.04 0.329296
d_loss | 1.80 0.555426
growth | 1.55 0.647143
ta | 1.29 0.777585
-------------+----------------------
Mean VIF | 4.48
MULTIKOLINEARITAS
. xtserial abs_dac spesialisasi big4 lev growth
sizelnta d_loss ta
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 102) = 8.254
Prob > F = 0.0049
Hasil: tolak h0: tidak ada autokorelasi
PENGUJIAN BLUE - AUTOKORELASI
Cross-sectional time-series FGLS regression
Coefficients: generalized least squares
Panels: homoskedastic
Correlation: no autocorrelation
Estimated covariances = 1 Number of obs = 309
Estimated autocorrelations = 0 Number of groups = 103
Estimated coefficients = 110 Time periods = 3
Wald chi2(109) = 351.14
Log likelihood = 335.3974 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
abs_dac | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spesialisasi | .042161 .0193632 2.18 0.029 .0042099 .0801122
big4 | -.0376224 .0332054 -1.13 0.257 -.1027038 .027459
lev | .0091587 .0197397 0.46 0.643 -.0295304 .0478479
growth | .0069892 .0044543 1.57 0.117 -.001741 .0157194
sizelnta | .0009292 .0169695 0.05 0.956 -.0323303 .0341887
d_loss | -.0259335 .0174447 -1.49 0.137 -.0601244 .0082574
ta | 9.11e-10 2.00e-09 0.46 0.649 -3.01e-09 4.83e-09
|
id |
2 | .2898759 .0911637 3.18 0.001 .1111984 .4685534
3 | -.0168839 .0709854 -0.24 0.812 -.1560128 .122245
4 | .0205374 .0696951 0.29 0.768 -.1160624 .1571372
5 | -.0010202 .0702496 -0.01 0.988 -.138707 .1366666
6 | -.0194081 .0681189 -0.28 0.776 -.1529187 .1141024
7 | -.0211798 .0736707 -0.29 0.774 -.1655717 .1232121
8 | -.0312662 .0686183 -0.46 0.649 -.1657556 .1032232
9 | .000552 .0813914 0.01 0.995 -.1589722 .1600762
10 | -.0356846 .0835675 -0.43 0.669 -.1994739 .1281046
11 | .0205514 .0670587 0.31 0.759 -.1108812 .1519839
SOLUSI: GLS
. swilk abs_dac spesialisasi big4 lev growth sizelnta d_loss ta
Shapiro-Wilk W test for normal data
Variable | Obs W V z Prob>z
-------------+--------------------------------------------------
abs_dac | 309 0.71310 62.746 9.730 0.00000
spesialisasi | 309 0.99915 0.187 -3.945 0.99996
big4 | 309 0.96979 6.607 4.439 0.00000
lev | 309 0.71754 61.775 9.693 0.00000
growth | 309 0.78150 47.787 9.089 0.00000
sizelnta | 309 0.98845 2.526 2.178 0.01470
d_loss | 309 0.98975 2.242 1.898 0.02887
ta | 309 0.36027 139.913 11.615 0.00000
H0: normal
H1: t idak normal
J ika prob>z leb ih kec i l dar i a l fa , re ject h0 t idak normal
hasi l : yang normal cuma spes ia l isas i
UJI NORMALITAS
. xtsu m abs_dac s pesiali sasi big4 le v growt h sizelnta d _loss t a
Variab le | M ean Std. D ev. Min Max | Observat ions
------ ----------- + ------- ------------ ------- ------------ ------ + ------------ ----
abs_da c overall | .1050 162 .11964 94 0 1.04 | N = 309
between | .08608 39 .0 133333 .49 | n = 103
within | .08338 87 -.2 549838 .65 50162 | T = 3
| |
spesia ~i overall | .592 233 .49221 66 0 1 | N = 309
between | .4224 94 0 1 | n = 103
within | .25482 36 -.0 744337 1 .2589 | T = 3
| |
big4 overall | .8608 414 .34667 33 0 1 | N = 309
between | .31145 27 0 1 | n = 103
within | .15430 33 .1 941748 1.5 27508 | T = 3
| |
lev overall | .7390 615 .57258 76 .03 4.6 | N = 309
between | .51887 04 .04 3.0 83333 | n = 103
within | .24572 01 -1. 717605 2.2 82395 | T = 3
| |
growth overall | 1.18 822 1.7973 24 -6.51 9.64 | N = 309
between | 1.4530 33 -2. 263333 7.85 | n = 103
within | 1.0643 21 -3. 058447 5.8 51554 | T = 3
| |
sizeln ta overall | 20.35 123 1.3651 64 17.35 24.79 | N = 309
between | 1.3314 98 17 .43333 24. 12667 | n = 103
within | .31984 27 18 .22123 22. 05123 | T = 3
| |
d_loss overall | .2750 809 .44727 94 0 1 | N = 309
between | .35371 41 0 1 | n = 103
within | .27524 09 -.3 915858 .94 17476 | T = 3
| |
ta overall | 2067 115 51131 86 34 311.14 5.8 3e+07 | N = 309
between | 42609 26 37 244.15 3.0 3e+07 | n = 103
within | 28472 92 -1. 55e+07 3.7 0e+07 | T = 3
STATISTIK DESKRIPTIF
. sum abs_dac spesialisasi big4 lev growth sizelnta d_loss ta
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
abs_dac | 309 .1050162 .1196494 0 1.04
spesialisasi | 309 .592233 .4922166 0 1
big4 | 309 .8608414 .3466733 0 1
lev | 309 .7390615 .5725876 .03 4.6
growth | 309 1.18822 1.797324 -6.51 9.64
-------------+--------------------------------------------------------
sizelnta | 309 20.35123 1.365164 17.35 24.79
d_loss | 309 .2750809 .4472794 0 1
ta | 309 2067115 5113186 34311.14 5.83e+07
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