my data: panel data, 260 observations; 20 Regions and time period: 2004 to 2016.
I have found endogeneity between corruption and GDP growth and I have chosen FE over RE because of the results of the Hausman test. Also, I chose LSDV over FE-2SLS because I would like to see the dummy coefficients.
Hence, I am running a LSDV model where I want to see the effect that the Corruption level (coded as Cor) of each Region(coded as countrynum) has on Y(which is GDP growth rate).
I do not understand why the model is significant with i.countrynum, but it becomes insignificant when I include i.Year. Also, when using i.countrynum, why would my variable of corruption become insignificant when I use log(corruption) instead of Corruption?
Also, I have run testparm on i.countrynum and i.year and the p values are close to 0.
Code:
. reg Y I logYlevel_1 n H Cor i.countrynum, robust
Linear regression Number of obs = 240
F(24, 215) = 4.04
Prob > F = 0.0000
R-squared = 0.2233
Root MSE = .02424
------------------------------------------------------------------------------
| Robust
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
I | .0421328 .0235268 1.79 0.075 -.00424 .0885055
logYlevel_1 | -.2725869 .0477639 -5.71 0.000 -.3667323 -.1784415
n | -.0011075 .0002068 -5.36 0.000 -.0015151 -.0007
H | -.5249337 .1279176 -4.10 0.000 -.777067 -.2728005
Cor | 152.1827 43.705 3.48 0.001 66.03756 238.3279
|
countrynum |
2 | -.0568746 .018295 -3.11 0.002 -.092935 -.0208142
3 | -.1177966 .0196517 -5.99 0.000 -.1565313 -.079062
4 | -.132077 .0269762 -4.90 0.000 -.1852487 -.0789053
5 | .0762482 .0194006 3.93 0.000 .0380084 .1144879
6 | .0528407 .0149889 3.53 0.001 .0232966 .0823847
7 | .0857303 .0238629 3.59 0.000 .0386951 .1327655
8 | .0685547 .0154954 4.42 0.000 .0380124 .099097
9 | .0485808 .0342438 1.42 0.157 -.0189157 .1160773
10 | .0277995 .01135 2.45 0.015 .0054279 .0501711
11 | -.0446546 .0128474 -3.48 0.001 -.0699775 -.0193316
12 | .0164898 .0176551 0.93 0.351 -.0183094 .051289
13 | -.129616 .0228253 -5.68 0.000 -.1746061 -.084626
14 | -.0697799 .0127563 -5.47 0.000 -.0949234 -.0446365
15 | -.1306724 .0235665 -5.54 0.000 -.1771235 -.0842214
16 | .0428457 .0137236 3.12 0.002 .0157956 .0698958
17 | .1167549 .021726 5.37 0.000 .0739316 .1595782
18 | .0173388 .0115789 1.50 0.136 -.0054839 .0401616
19 | .0970503 .0240081 4.04 0.000 .0497288 .1443717
20 | .0306902 .0163222 1.88 0.061 -.0014819 .0628623
|
_cons | 2.812614 .4884072 5.76 0.000 1.849934 3.775293
------------------------------------------------------------------------------
Code:
. reg Y I logYlevel_1 n H logCor i.countrynum,robust
Linear regression Number of obs = 225
F(24, 200) = 4.83
Prob > F = 0.0000
R-squared = 0.2391
Root MSE = .0232
------------------------------------------------------------------------------
| Robust
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
I | .0438102 .023208 1.89 0.061 -.0019535 .0895738
logYlevel_1 | -.2952572 .0474944 -6.22 0.000 -.3889112 -.2016031
n | -.0010141 .0001769 -5.73 0.000 -.001363 -.0006652
H | -.5967442 .1222761 -4.88 0.000 -.83786 -.3556284
logCor | -.0001871 .002452 -0.08 0.939 -.0050222 .004648
|
countrynum |
2 | -.0776557 .0163377 -4.75 0.000 -.109872 -.0454395
3 | -.1267072 .0195275 -6.49 0.000 -.1652135 -.0882009
4 | -.1387356 .0270324 -5.13 0.000 -.1920407 -.0854305
5 | .0833008 .0189025 4.41 0.000 .046027 .1205746
6 | .0572389 .0149254 3.83 0.000 .0278075 .0866703
7 | .0958031 .0238787 4.01 0.000 .0487167 .1428895
8 | .0765805 .0158141 4.84 0.000 .0453967 .1077642
9 | .0565331 .0340127 1.66 0.098 -.0105365 .1236026
10 | .030444 .011336 2.69 0.008 .0080905 .0527975
11 | -.0304818 .0129706 -2.35 0.020 -.0560585 -.0049051
12 | .0196087 .0175451 1.12 0.265 -.0149885 .0542058
13 | -.139783 .0227806 -6.14 0.000 -.1847039 -.0948621
14 | -.0761153 .0127299 -5.98 0.000 -.1012175 -.0510132
15 | -.1402884 .0235798 -5.95 0.000 -.1867854 -.0937915
16 | .0473936 .0139128 3.41 0.001 .019959 .0748281
17 | .1254732 .021914 5.73 0.000 .082261 .1686854
18 | .0189846 .0122194 1.55 0.122 -.0051108 .0430799
19 | .1095675 .0269841 4.06 0.000 .0563576 .1627774
20 | .0338714 .0162419 2.09 0.038 .0018441 .0658987
|
_cons | 3.049778 .4834469 6.31 0.000 2.096471 4.003085
------------------------------------------------------------------------------
Code:
. reg Y I logYlevel_1 n H Cor i.countrynum i.Year, robust
Linear regression Number of obs = 240
F(35, 204) = 19.07
Prob > F = 0.0000
R-squared = 0.7447
Root MSE = .01427
------------------------------------------------------------------------------
| Robust
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
I | -.0017374 .0152018 -0.11 0.909 -.0317101 .0282353
logYlevel_1 | -.1344197 .043968 -3.06 0.003 -.2211097 -.0477297
n | -.0000933 .0003057 -0.31 0.761 -.0006961 .0005095
H | .1089849 .1461164 0.75 0.457 -.179107 .3970768
Cor | 42.22968 27.68753 1.53 0.129 -12.36074 96.82009
|
countrynum |
2 | -.0151863 .0160518 -0.95 0.345 -.046835 .0164625
3 | -.0482293 .0157838 -3.06 0.003 -.0793496 -.017109
4 | -.0381451 .0174077 -2.19 0.030 -.0724672 -.003823
5 | .0448873 .0187381 2.40 0.018 .0079421 .0818325
6 | .0300398 .0107687 2.79 0.006 .0088075 .0512721
7 | .0336804 .0214411 1.57 0.118 -.0085941 .075955
8 | .0259668 .011634 2.23 0.027 .0030284 .0489052
9 | .0567836 .0316731 1.79 0.074 -.005665 .1192323
10 | .0101893 .0064318 1.58 0.115 -.0024919 .0228706
11 | -.0263025 .0101509 -2.59 0.010 -.0463165 -.0062884
12 | .0268867 .0158145 1.70 0.091 -.0042941 .0580675
13 | -.0394516 .0159554 -2.47 0.014 -.0709102 -.007993
14 | -.0212305 .0097003 -2.19 0.030 -.0403562 -.0021048
15 | -.0406477 .0160236 -2.54 0.012 -.0722408 -.0090546
16 | .026545 .0117709 2.26 0.025 .0033367 .0497533
17 | .0621137 .0196756 3.16 0.002 .02332 .1009073
18 | -.0060011 .0064424 -0.93 0.353 -.0187033 .006701
19 | .049247 .0188249 2.62 0.010 .0121307 .0863634
20 | .0361759 .0169272 2.14 0.034 .0028012 .0695507
|
Year |
2006 | .0145628 .0034744 4.19 0.000 .0077125 .0214131
2007 | .0062804 .0040101 1.57 0.119 -.0016263 .014187
2008 | -.0235486 .005161 -4.56 0.000 -.0337243 -.013373
2009 | -.0631985 .0054573 -11.58 0.000 -.0739584 -.0524386
2010 | -.0056302 .0071231 -0.79 0.430 -.0196746 .0084142
2011 | -.0116207 .0053297 -2.18 0.030 -.022129 -.0011123
2012 | -.0420052 .006943 -6.05 0.000 -.0556944 -.0283159
2013 | -.0412766 .0104555 -3.95 0.000 -.0618912 -.0206619
2014 | -.0257527 .0091024 -2.83 0.005 -.0436995 -.0078059
2015 | -.0074938 .0120268 -0.62 0.534 -.0312067 .0162191
2016 | -.0120389 .0093528 -1.29 0.199 -.0304795 .0064017
|
_cons | 1.349846 .4464339 3.02 0.003 .4696303 2.230063
------------------------------------------------------------------------------
Code:
reg Y I logYlevel_1 n H Cor i.Year, robust
Linear regression Number of obs = 240
F(16, 223) = 36.46
Prob > F = 0.0000
R-squared = 0.7030
Root MSE = .01472
------------------------------------------------------------------------------
| Robust
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
I | .0002541 .0022204 0.11 0.909 -.0041216 .0046298
logYlevel_1 | .0047408 .0051855 0.91 0.362 -.0054781 .0149596
n | -.0002482 .0002666 -0.93 0.353 -.0007736 .0002772
H | -.0516428 .0513114 -1.01 0.315 -.1527602 .0494745
Cor | .9710216 20.83673 0.05 0.963 -40.09107 42.03312
|
Year |
2006 | .0150445 .0029372 5.12 0.000 .0092562 .0208328
2007 | .0066831 .0030496 2.19 0.029 .0006734 .0126929
2008 | -.0241049 .0040395 -5.97 0.000 -.0320654 -.0161444
2009 | -.0608203 .0044387 -13.70 0.000 -.0695674 -.0520731
2010 | .0056591 .0052307 1.08 0.280 -.0046489 .0159672
2011 | -.0011653 .0038145 -0.31 0.760 -.0086823 .0063518
2012 | -.0292677 .0043704 -6.70 0.000 -.0378802 -.0206552
2013 | -.0219385 .0081194 -2.70 0.007 -.037939 -.005938
2014 | -.0047909 .0041872 -1.14 0.254 -.0130425 .0034608
2015 | .0144844 .0063197 2.29 0.023 .0020304 .0269385
2016 | .0083592 .0044414 1.88 0.061 -.0003933 .0171117
|
_cons | -.0385648 .0492351 -0.78 0.434 -.1355904 .0584608
------------------------------------------------------------------------------
0 Response to LSDV, problem with dummy variables and significance
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