Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float sd8st byte(winpowersh winpowercus powerstaff pwrpublic) float(winpowertv logta) double winroe float logtobin double(debt age) byte gbi
2.2916667 0 0 0 0 0 14.239168 .05294823145435599 -.31732535 .46726300753238215 30.833333333333332 0
4.064394 0 0 0 0 0 14.506706 .08026226252787182 -.19081886 .5317689494514303 31.083333333333336 0
2.791667 0 0 0 0 0 10.991343 -.04889071865239451 .7466849 .18255332052966744 18.25 2
2.2083333 0 0 0 0 0 11.921798 .06746456612623636 .015695516 .4787392367385989 13.25 0
4.3360357 0 0 0 0 0 13.87127 .15350707801002095 .29696715 .3722 4.083333333333333 0
2.395833 0 0 0 0 0 11.987264 .15350707801002095 -.3629666 .1688851551075018 26.166666666666668 0
2.2083333 0 0 0 0 0 14.266216 .06415065826940003 -.24985418 .5446232013248214 15.166666666666668 0
2.4375 0 0 0 0 0 13.624822 .0071320555442205445 -.58335215 .19339779038952554 26 0
2.666667 0 0 0 0 0 14.173757 .08687234638901288 -.27547094 .4613930362862616 29.833333333333332 0
1.5833334 0 0 0 0 0 12.579242 -.04889071865239451 -.06245073 .5052570696742475 23.083333333333332 0
2 0 0 0 0 0 14.207354 -.048065127560500805 -.4244451 .4199003045199519 30.083333333333336 0
2.770833 0 0 0 0 0 12.740792 .15350707801002095 -.50385123 .12091307990181432 32.083333333333336 0
2.375 0 0 0 0 0 12.73841 .10905893738050841 -.3549458 .2536246777574857 31.083333333333336 0
2.375 0 0 0 0 0 12.67783 .10057880254293576 -.1722317 .27671402100061765 30.083333333333336 0
2.3333333 0 0 0 0 0 11.93374 .09452965382291158 -.12927127 .5437106092436975 27 0
1.6666666 0 0 0 0 0 13.211438 .14282739760084529 .3177111 .4112857929565792 13 0
1.5833334 0 0 0 0 0 13.137717 .009517116053677933 -.4150076 .4927757732364851 12 0
2.395833 0 0 0 0 0 13.591644 .025474341822720827 -.56330365 .1902674682458076 25 0
1.9829545 0 0 0 0 0 14.183026 -.028286338676147077 -.4342451 .4277529495021958 31.083333333333336 0
1.7708334 0 0 0 0 0 12.989533 .05996759443454595 -.22175293 .6038733962682187 16.25 0
2.625 0 0 0 0 0 12.058269 .03799204634042847 .23281607 .41260723394389054 20.25 0
1.9375 0 0 0 0 0 11.290694 .06610567305897101 -.4949625 .06352952930450964 23.583333333333332 0
2.0625 0 0 0 0 0 11.311886 -.04889071865239451 .02170962 .4655939746662102 25.666666666666668 0
2.0625 0 0 0 0 0 11.46179 -.04889071865239451 -.08427203 .5200968268168183 24.666666666666668 0
3.416667 0 0 0 0 0 14.473316 .046336797237120274 -.4559155 .3967621695763699 23.75 0
2.2083333 0 0 0 0 0 11.774474 .15350707801002095 -.0037098925 .27496266186274965 14.25 0
1.6666666 0 0 0 0 0 13.101131 -.04889071865239451 -.25075278 .6959423570366254 21.25 0
1.7916666 0 0 0 0 0 14.041067 .08980034033519187 -.10520446 .7082871781775231 31.083333333333336 0
2.8125 0 0 0 0 0 14.511697 .041881297017547336 -.06134725 .5476896066820631 30.833333333333332 0
2.25 0 0 0 0 0 13.266334 .09021447858807585 .06207087 .15660908343074859 28.333333333333332 0
3.291667 0 0 0 0 0 14.25218 .15350707801002095 -.4532536 .4882681925242885 30.083333333333336 0
3.0492425 0 0 0 0 0 13.215508 .027590464333851202 .0145885 .11451139738707385 29.333333333333332 0
3.632576 0 0 0 0 0 13.169182 .06387687845318817 -.6117204 .29463743830706574 29.666666666666668 0
2.2083333 0 0 0 0 0 10.53327 -.04889071865239451 .6827319 .5799861495844876 18.333333333333332 0
2.583333 0 0 0 0 0 12.849533 -.04889071865239451 .0576499 .8199500277185893 19.583333333333332 0
2.848485 0 0 0 0 0 13.06875 .15350707801002095 .2248707 .5879826967714707 15.25 0
1.9166666 0 0 0 0 0 12.78078 .10942161305109072 -.07265077 .25094984267790904 19.833333333333332 0
2.8125 0 0 0 0 0 14.442632 .021242555099128112 -.07928707 .5329463047206702 29.833333333333332 0
2.708333 0 0 0 0 0 13.304167 .04167297490656383 .10697688 .4382738386063443 15 0
2.2954545 0 0 0 0 0 13.85133 .10233340107524895 -.22423366 .42555197073136847 16.583333333333332 0
2.2291667 0 0 0 0 0 12.970284 .058325406042638125 2.3264396 .20202579984166164 22.666666666666668 0
2.3333333 0 0 0 0 0 12.952185 .06286667814432717 -.4549696 .2838625085060829 21.083333333333332 0
2.278409 0 0 0 0 0 14.233975 .06371479245060213 -.2084066 .7102229691891497 26.75 0
3.291667 0 0 0 0 0 13.1943 .08143609549505651 .26630947 .38123705315349354 14 0
3.753788 0 0 0 0 0 14.286483 .010246165967668273 -.54395086 .462697722395353 32.083333333333336 2
3.753247 0 0 0 0 0 14.45894 .07597470241436134 -.10019578 .5318768572697248 30.083333333333336 1
2.2121212 0 0 0 0 0 11.318455 .006842140829841871 -.7036114 .28101351925856644 21.833333333333332 0
1.9375 0 0 0 0 0 11.3944 -.01985668652335319 -.6049169 .3479729349380228 20.833333333333332 0
1.7916666 0 0 0 0 0 13.99624 .030343159988526484 -.1098341 .6886435910490865 32.083333333333336 0
3.102273 0 0 0 0 0 14.854198 .07067050862286085 -.04931026 .5728110807841716 18.333333333333332 0
2.0359848 0 0 0 0 0 12.783222 .021711007368243433 -.53942055 .45094345449491985 19.333333333333332 0
2.003788 0 0 0 0 0 13.07035 -.04889071865239451 -.21019833 .41360095057915386 30.833333333333332 0
1.6666666 0 0 0 0 0 13.91102 .15350707801002095 -.1094732 .7032975223137191 30.083333333333336 0
2.9242425 0 0 0 0 0 14.464911 .0769787608060392 -.27564496 .4508630150653497 18 0
3.125 0 0 0 0 0 11.860895 -.04784118410438076 .04825909 .3960344304083492 19.25 0
2.153409 0 0 0 0 0 13.58439 .017061994263510884 -.4030683 .4104628907455871 32.083333333333336 1
3.2329545 0 0 0 0 0 13.719903 .15350707801002095 .5943209 .5598440176888518 32.083333333333336 0
2.852273 0 0 0 0 0 17.952454 .07195409371835504 .2088956 .46532004708430297 1 0
4.3777056 0 0 0 0 0 16.136366 .0340006103167885 -.0015164106 .5160640592687961 32.083333333333336 2
2.541667 0 0 0 0 0 12.780442 .0673387140972849 -.15952496 .2294778745615171 20.833333333333332 0
2.2121212 0 0 0 0 0 12.950326 .06970238184574284 -.5939475 .2086328371823438 13.416666666666666 0
2.791667 0 0 0 0 0 13.266384 .04703599377336313 -.3428546 .34586651057919765 25.583333333333332 0
2.3333333 0 0 0 0 0 14.724997 .1321413777998075 -.3220526 .18912449099835807 15 0
1.7083334 0 0 0 0 0 12.58096 .06519879914546459 -.2327994 .023490717128461746 20.083333333333332 0
1.9375 0 0 0 0 0 12.991263 .04478105205177637 -.24980025 .5860362774047176 17.25 0
1.9375 0 0 0 0 0 11.427063 .017528843849498443 -.509178 .3634441877696901 19.833333333333332 0
2.729167 0 0 0 0 0 13.75691 .15350707801002095 -.04923254 .42568636515941316 15.583333333333332 0
1.5 0 0 0 0 0 12.521963 .15350707801002095 -.5866695 .24713083676028816 24.083333333333332 0
2.729167 0 0 0 0 0 13.612513 .062146640944874876 -.5204086 .17508820502768657 24 0
2.25 0 0 0 0 0 14.55436 .039354715338151075 -.3623687 .5578723772156966 16.166666666666668 0
1.9166666 0 0 0 0 0 11.950264 .09177624936151005 -.064155445 .4563858502628214 18.75 0
2.375 0 0 0 0 0 13.21545 .09364049113472159 2.0353673 .2506801462883966 24.666666666666668 0
1.9166666 0 0 0 0 0 11.214816 -.01361548215436423 -.1838209 .35184985583789175 23.666666666666668 0
2.395833 0 0 0 0 0 12.034234 .13235767945869864 -.11571672 .2997512216264999 25.166666666666668 0
2.0625 0 0 0 0 0 10.052296 -.04889071865239451 -.16605575 .25976991684260414 20.916666666666668 0
2.458333 0 0 0 0 0 13.127112 .038776167760066586 .1029391 .5551932799872606 24.5 0
2.4375 0 0 0 0 0 11.666744 .041741605008537276 .4663056 .2467503515450835 19.75 0
2.3333333 0 0 0 0 0 11.730067 .0848704552724171 -.14743829 .4716141390078228 26 0
2.395833 0 0 0 0 0 11.979818 .03412034747695419 -.413039 .15054262302276447 27.166666666666668 0
2.897727 0 0 0 0 0 14.767964 .06029360346859468 -.1323448 .5950534381301689 17.166666666666668 0
1.6875 0 0 0 0 0 13.239556 -.04889071865239451 -.385875 .5851916153178409 20.25 0
3.598485 0 0 0 0 0 15.803253 .03294424095663211 -.31807145 .5319953122589579 25.833333333333332 0
1.9375 0 0 0 0 0 11.232338 -.04889071865239451 -.7393987 .03297852622659994 24.583333333333332 0
2.715909 0 0 0 0 0 18.021233 .0636237897648686 .33071935 .4286630186822526 1.5833333333333333 0
1.5 0 0 0 0 0 13.277675 .013656057890796906 .1878538 .16968516003979378 20.75 0
2.965909 0 0 0 0 0 14.427778 .08729609342081503 -.1768751 .4596256119179664 17 0
end
Code:
hausman fixed
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed . Difference S.E.
-------------+----------------------------------------------------------------
winpowersh | -.2715399 -.3195323 .0479923 .0918443
winpowercus | .1962471 .2391097 -.0428626 .0459824
powerstaff | .0357576 .1057848 -.0700272 .0194739
winpowertv | .3212632 .4606821 -.1394189 .0703318
pwrpublic | .066142 .1214229 -.0552808 .0167976
logta | .0342156 .188359 -.1541433 .0965926
winroe | -.8901204 -.8233513 -.0667691 .2650299
logtobin | -.0942411 .0914538 -.185695 .1543165
debt | -.0845396 -.0157862 -.0687534 .28107
ageyr | .1058631 .0109533 .0949098 .0261027
gbi | .0630154 .128724 -.0657086 .0117695
------------------------------------------------------------------------------
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(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 64.86
Prob>chi2 = 0.0000
I first run a single regression model which is
Code:
xtreg sd8st fivetotpower , fe vce(robust)
Code:
xtreg sd8st winpowersh winpowercus powerstaff winpowertv pwrpublic logta winroe logtobin debt ageyr gbi, fe vce (robust)
The fixed effect regression result shows that most of the predictor variables are not significant.
Code:
Fixed-effects (within) regression Number of obs = 420
Group variable: code Number of groups = 140
R-sq: Obs per group:
within = 0.1790 min = 3
between = 0.0205 avg = 3.0
overall = 0.0246 max = 3
F(11,139) = 4.89
corr(u_i, Xb) = -0.7342 Prob > F = 0.0000
(Std. Err. adjusted for 140 clusters in code)
------------------------------------------------------------------------------
| Robust
sd8st | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
winpowersh | -.2715399 .2423346 -1.12 0.264 -.7506786 .2075987
winpowercus | .1962471 .160246 1.22 0.223 -.1205877 .5130819
powerstaff | .0357576 .0451849 0.79 0.430 -.053581 .1250962
winpowertv | .3212632 .2146548 1.50 0.137 -.1031475 .7456739
pwrpublic | .066142 .036404 1.82 0.071 -.0058352 .1381193
logta | .0342156 .1768392 0.19 0.847 -.3154269 .3838582
winroe | -.8901204 .7250859 -1.23 0.222 -2.323744 .5435034
logtobin | -.0942411 .1710528 -0.55 0.583 -.4324429 .2439606
debt | -.0845396 .4504337 -0.19 0.851 -.9751271 .8060479
ageyr | .1058631 .0301443 3.51 0.001 .0462625 .1654638
gbi | .0630154 .0550184 1.15 0.254 -.0457658 .1717966
_cons | .0862713 1.986048 0.04 0.965 -3.840498 4.013041
-------------+----------------------------------------------------------------
sigma_u | 1.0222132
sigma_e | .37382097
rho | .88204052 (fraction of variance due to u_i)
I run some descriptive statistics to have an idea what is my data pointed me to.
Code:
xtsum winpowersh winpowercus powerstaff winpowertv pwrpublic logta winroe logtobin debt age gbi
Variable | Mean Std. Dev. Min Max | Observations
-----------------+--------------------------------------------+----------------
winpow~h overall | .0571429 .2323922 0 1 | N = 420
between | .1996914 0 1 | n = 140
within | .1196654 -.6095238 .7238095 | T = 3
| |
winpow~s overall | .0952381 .2938936 0 1 | N = 420
between | .2375121 0 1 | n = 140
within | .1738698 -.5714286 .7619048 | T = 3
| |
powers~f overall | .9452381 .9693888 0 3 | N = 420
between | .8190485 0 3 | n = 140
within | .5216094 -.7214286 2.945238 | T = 3
| |
winpow~v overall | .0142857 .1188076 0 1 | N = 420
between | .0786723 0 .6666667 | n = 140
within | .0891933 -.652381 .6809524 | T = 3
| |
pwrpub~c overall | 1.37619 1.140031 0 3 | N = 420
between | .975186 0 3 | n = 140
within | .5943248 -.6238095 3.37619 | T = 3
| |
logta overall | 13.71069 1.324127 10.01534 18.02123 | N = 420
between | 1.310805 10.05519 17.96488 | n = 140
within | .2080917 12.09197 14.69014 | T = 3
| |
winroe overall | .0520414 .0608951 -.0488907 .1535071 | N = 420
between | .0508236 -.0488907 .1535071 | n = 140
within | .0337269 -.0551456 .1869733 | T = 3
| |
logtobin overall | -.1330402 .3817596 -1.002916 2.32644 | N = 420
between | .3689297 -.8758939 2.171567 | n = 140
within | .1013951 -.4634964 .2710581 | T = 3
| |
debt overall | .4473444 .187458 .0234907 .9146833 | N = 420
between | .1791725 .0498293 .8563605 | n = 140
within | .0564884 .0403117 .6828321 | T = 3
| |
age overall | 21.15913 7.961744 .5 32.08333 | N = 420
between | 7.938848 1.027778 31.08333 | n = 140
within | .8154065 20.15913 22.15913 | T = 3
| |
gbi overall | .147619 .579786 0 5 | N = 420
between | .3943474 0 2.333333 | n = 140
within | .4258923 -2.185714 2.814286 | T = 3
Thank you.
0 Response to Predictor variables became not significant in fixed effect model.
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