i am running a regression using panel data and looking at the effect of company financial data and characteristics on tax adjustments. The tax adjustments paid in period t are caused in periods before (t-1 oder t-2) and were found after tax audit. I don´t have many observations per group and i want to control for different company characteristics (dummy variables). The effects are primarily between the groups not within the time series. In my opinion the RE Model would be the logical consequence, but the hausman test rejects H0 so i can´t use RE.
This is how my xtreg, fe regression looks like:
Code:
Code:
xtreg LOG_STN l1.LOG_SALES l2.LOG_SALES l1.PROV l2.PROV l1.LEV l2.LEV l1.DEBT l2.DEBT l1.ROA l2.ROA l1.IVG_intense l2.IVG_intense l1.KAP_intense l2.KAP_intense l1.VOR_intense l
> 2.VOR_intense FamUN AUSL_KSTR y2012 y2013 y2014 y2015 y2016 y2017 bw bay hes sac nrw c f g m n, fe
note: FamUN omitted because of collinearity
note: AUSL_KSTR omitted because of collinearity
note: bw omitted because of collinearity
note: bay omitted because of collinearity
note: hes omitted because of collinearity
note: sac omitted because of collinearity
note: nrw omitted because of collinearity
note: c omitted because of collinearity
note: f omitted because of collinearity
note: g omitted because of collinearity
note: m omitted because of collinearity
note: n omitted because of collinearity
Fixed-effects (within) regression Number of obs = 3,518
Group variable: idc Number of groups = 2,189
R-sq: Obs per group:
within = 0.0216 min = 1
between = 0.0001 avg = 1.6
overall = 0.0002 max = 6
F(22,1307) = 1.31
corr(u_i, Xb) = -0.1703 Prob > F = 0.1501
------------------------------------------------------------------------------
LOG_STN | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
LOG_SALES |
L1. | .8047563 .3752725 2.14 0.032 .0685539 1.540959
L2. | -.7372711 .3916178 -1.88 0.060 -1.505539 .0309972
|
PROV |
L1. | -.2168305 1.86602 -0.12 0.908 -3.877553 3.443892
L2. | 2.515237 1.810256 1.39 0.165 -1.036087 6.066562
|
LEV |
L1. | .0663668 .0781916 0.85 0.396 -.087028 .2197616
L2. | -.066027 .0374569 -1.76 0.078 -.1395093 .0074553
|
DEBT |
L1. | -.0293448 .6116295 -0.05 0.962 -1.229228 1.170538
L2. | .3254192 .5211656 0.62 0.532 -.6969934 1.347832
|
ROA |
L1. | -.6775415 1.158015 -0.59 0.559 -2.949314 1.594231
L2. | -2.684723 1.101758 -2.44 0.015 -4.84613 -.5233155
|
IVG_intense |
L1. | 1.390094 2.55196 0.54 0.586 -3.616293 6.39648
L2. | -1.74212 2.698345 -0.65 0.519 -7.03568 3.551441
|
KAP_intense |
L1. | -.0716247 1.594696 -0.04 0.964 -3.200068 3.056818
L2. | -.6143406 1.284293 -0.48 0.632 -3.133843 1.905161
|
VOR_intense |
L1. | 1.185455 .9368948 1.27 0.206 -.652527 3.023437
L2. | .800335 .8815385 0.91 0.364 -.9290501 2.52972
|
FamUN | 0 (omitted)
AUSL_KSTR | 0 (omitted)
y2012 | -.1148903 .2005894 -0.57 0.567 -.5084027 .2786221
y2013 | -.2193175 .2076353 -1.06 0.291 -.6266523 .1880173
y2014 | -.3174528 .2204653 -1.44 0.150 -.7499573 .1150517
y2015 | -.2075198 .233365 -0.89 0.374 -.6653308 .2502912
y2016 | -.3926721 .2435855 -1.61 0.107 -.8705334 .0851892
y2017 | -.2898164 .3278856 -0.88 0.377 -.9330559 .3534232
bw | 0 (omitted)
bay | 0 (omitted)
hes | 0 (omitted)
sac | 0 (omitted)
nrw | 0 (omitted)
c | 0 (omitted)
f | 0 (omitted)
g | 0 (omitted)
m | 0 (omitted)
n | 0 (omitted)
_cons | 1.903193 4.79805 0.40 0.692 -7.509529 11.31591
-------------+----------------------------------------------------------------
sigma_u | 3.7078212
sigma_e | 2.0251375
rho | .77023059 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(2188, 1307) = 2.12 Prob > F = 0.0000
Code:
Test: Ho: difference in coefficients not systematic
chi2(22) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 76.77
Prob>chi2 = 0.0000
Code:
xtreg LOG_STN l1.LOG_SALES l2.LOG_SALES l1.PROV l2.PROV l1.LEV l2.LEV l1.DEBT l2.DEBT l1.ROA l2.ROA l1.IVG_intense l2.IVG_intense l1.KAP_intense l2.KAP_intense l1.VOR_intense l
> 2.VOR_intense FamUN AUSL_KSTR y2012 y2013 y2014 y2015 y2016 y2017 bw bay hes sac nrw c f g m n, re
Random-effects GLS regression Number of obs = 3,518
Group variable: idc Number of groups = 2,189
R-sq: Obs per group:
within = 0.0006 min = 1
between = 0.5251 avg = 1.6
overall = 0.5054 max = 6
Wald chi2(34) = 2512.84
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
LOG_STN | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
LOG_SALES |
L1. | .965822 .1913759 5.05 0.000 .5907322 1.340912
L2. | .1194994 .1908272 0.63 0.531 -.2545151 .4935139
|
PROV |
L1. | 1.435207 1.127386 1.27 0.203 -.7744284 3.644843
L2. | -.734266 1.142288 -0.64 0.520 -2.973109 1.504578
|
LEV |
L1. | -.0132113 .027322 -0.48 0.629 -.0667613 .0403388
L2. | .0014444 .0193577 0.07 0.941 -.0364961 .0393849
|
DEBT |
L1. | -.1789497 .3023988 -0.59 0.554 -.7716405 .4137411
L2. | -.436075 .2741722 -1.59 0.112 -.9734427 .1012927
|
ROA |
L1. | -.1335986 .6398764 -0.21 0.835 -1.387733 1.120536
L2. | -1.52944 .6135618 -2.49 0.013 -2.731998 -.3268806
|
IVG_intense |
L1. | .0267801 1.624745 0.02 0.987 -3.157661 3.211221
L2. | 1.663602 1.592992 1.04 0.296 -1.458605 4.785808
|
KAP_intense |
L1. | .2447735 .8247597 0.30 0.767 -1.371726 1.861273
L2. | 1.489765 .7936084 1.88 0.060 -.0656792 3.045208
|
VOR_intense |
L1. | -.3348842 .4761082 -0.70 0.482 -1.268039 .5982708
L2. | -.3350098 .460146 -0.73 0.467 -1.236879 .5668598
|
FamUN | -.5909862 .1461903 -4.04 0.000 -.8775138 -.3044586
AUSL_KSTR | .4322607 .13506 3.20 0.001 .1675479 .6969735
y2012 | -.0770948 .1694286 -0.46 0.649 -.4091688 .2549791
y2013 | -.3837821 .1674422 -2.29 0.022 -.7119628 -.0556015
y2014 | -.1691641 .1717899 -0.98 0.325 -.505866 .1675379
y2015 | .1384244 .1810782 0.76 0.445 -.2164823 .4933311
y2016 | .068587 .1829719 0.37 0.708 -.2900313 .4272053
y2017 | -.0408278 .2510171 -0.16 0.871 -.5328124 .4511567
bw | -.1638173 .1698556 -0.96 0.335 -.4967282 .1690935
bay | -.0336689 .161638 -0.21 0.835 -.3504736 .2831357
hes | .1426045 .2052941 0.69 0.487 -.2597646 .5449737
sac | .2499873 .2373464 1.05 0.292 -.2152031 .7151778
nrw | -.0404006 .1467679 -0.28 0.783 -.3280604 .2472591
c | -.5049047 .1654168 -3.05 0.002 -.8291157 -.1806938
f | -1.284322 .2200182 -5.84 0.000 -1.71555 -.8530946
g | -.8524794 .1796878 -4.74 0.000 -1.204661 -.5002978
m | -.2227419 .1809266 -1.23 0.218 -.5773516 .1318678
n | -.1955437 .3183816 -0.61 0.539 -.8195601 .4284727
_cons | -8.119061 .4488191 -18.09 0.000 -8.99873 -7.239392
-------------+----------------------------------------------------------------
sigma_u | 1.7691576
sigma_e | 2.0251375
rho | .43284126 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Code:
Breusch and Pagan Lagrangian multiplier test for random effects
LOG_STN[idc,t] = Xb + u[idc] + e[idc,t]
Estimated results:
| Var sd = sqrt(Var)
---------+-----------------------------
LOG_STN | 13.63771 3.692926
e | 4.101182 2.025137
u | 3.129919 1.769158
Test: Var(u) = 0
chibar2(01) = 89.51
Prob > chibar2 = 0.0000
0 Response to Model with Independent lagged variables: FE, RE or POLS?
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