I am working on a panel of 59 banks picked from 12 countries. My time period spans from 2009 till 2015. My objective is to figure out which determinants impact profit of banks. My dependent variable is therefore profit which takes the abbreviation 'nim' in my commands which I have pasted below. All other variables are explanatory variables. Country names represent country dummies.
I now need to perform 'Least Square Dummy Variables (LSDV) ' on this panel data. I have learnt from various textbooks that 'LSDV' is the other name for 'fixed effect estimation'. I need to know which of the following three commands performs LSDV in my panel data.
Command # 1:
Command # 2:
Command # 3:
Problem with the first command is that iy is omitting my country dummies. I don't want this to happen. I need to know their sign and significance.
Confusion with the second command is that it uses xtreg plus it doesn't use -fe- option.
I read LSDV is simply OLS applied on model that includes country dummies. So, is -xtreg- the right command or is the 3rd command that uses -reg- correct?'
Please reply! Which among these three commands actually perform LSDV in my model?
I now need to perform 'Least Square Dummy Variables (LSDV) ' on this panel data. I have learnt from various textbooks that 'LSDV' is the other name for 'fixed effect estimation'. I need to know which of the following three commands performs LSDV in my panel data.
Command # 1:
Code:
. xtreg nim nir cr lta otoi nlta ooiti eata car bm inf pcgg cpi Nigeria SriLanka Bangladesh India Kenya M
> alaysia Egypt Philippines China Turkey Thailand,fe
note: Nigeria omitted because of collinearity
note: SriLanka omitted because of collinearity
note: Bangladesh omitted because of collinearity
note: India omitted because of collinearity
note: Malaysia omitted because of collinearity
note: Egypt omitted because of collinearity
note: Philippines omitted because of collinearity
note: China omitted because of collinearity
note: Turkey omitted because of collinearity
Fixed-effects (within) regression Number of obs = 413
Group variable: id Number of groups = 59
R-sq: within = 0.3481 Obs per group: min = 7
between = 0.1971 avg = 7.0
overall = 0.2147 max = 7
F(14,340) = 12.97
corr(u_i, Xb) = -0.0951 Prob > F = 0.0000
------------------------------------------------------------------------------
nim | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nir | .0163953 .011685 1.40 0.161 -.0065888 .0393794
cr | .0016437 .0009959 1.65 0.100 -.0003151 .0036026
lta | -1.106441 .3945453 -2.80 0.005 -1.882498 -.3303838
otoi | -.0455696 .0052323 -8.71 0.000 -.0558613 -.0352779
nlta | -.0029255 .0072428 -0.40 0.687 -.0171718 .0113208
ooiti | -.0466676 .0053673 -8.69 0.000 -.0572249 -.0361104
eata | -.0176386 .0077747 -2.27 0.024 -.0329313 -.002346
car | .0168051 .0165491 1.02 0.311 -.0157464 .0493566
bm | .0007851 .0075308 0.10 0.917 -.0140277 .0155978
inf | .0199149 .0167545 1.19 0.235 -.0130407 .0528705
pcgg | -.0425883 .0169323 -2.52 0.012 -.0758936 -.0092829
cpi | -.0125125 .0130406 -0.96 0.338 -.038163 .0131379
Nigeria | 0 (omitted)
SriLanka | 0 (omitted)
Bangladesh | 0 (omitted)
India | 0 (omitted)
Kenya | .9214237 .7084233 1.30 0.194 -.4720206 2.314868
Malaysia | 0 (omitted)
Egypt | 0 (omitted)
Philippines | 0 (omitted)
China | 0 (omitted)
Turkey | 0 (omitted)
Thailand | -.8381735 .6967971 -1.20 0.230 -2.20875 .5324025
_cons | 14.05015 1.784493 7.87 0.000 10.54011 17.56019
-------------+----------------------------------------------------------------
sigma_u | 1.7229017
sigma_e | .63462076
rho | .88053173 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(58, 340) = 23.04 Prob > F = 0.0000
.
Code:
. xtreg nim nir cr lta otoi nlta ooiti eata car bm inf pcgg cpi Nigeria SriLanka Bangladesh India Kenya M
> alaysia Egypt Philippines China Turkey Thailand
Random-effects GLS regression Number of obs = 413
Group variable: id Number of groups = 59
R-sq: within = 0.3348 Obs per group: min = 7
between = 0.7206 avg = 7.0
overall = 0.6728 max = 7
Wald chi2(23) = 334.94
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
nim | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nir | .0187304 .0115449 1.62 0.105 -.0038972 .041358
cr | .0012113 .0009953 1.22 0.224 -.0007396 .0031621
lta | -.7070863 .2783553 -2.54 0.011 -1.252653 -.1615198
otoi | -.0395627 .0050475 -7.84 0.000 -.0494556 -.0296698
nlta | .002645 .0070117 0.38 0.706 -.0110978 .0163877
ooiti | -.0439643 .0053437 -8.23 0.000 -.0544377 -.0334908
eata | -.0217432 .0078519 -2.77 0.006 -.0371326 -.0063538
car | .0230583 .0161679 1.43 0.154 -.0086302 .0547468
bm | .0044242 .0077166 0.57 0.566 -.0107001 .0195485
inf | .0221639 .0171058 1.30 0.195 -.0113628 .0556907
pcgg | -.0461017 .0175316 -2.63 0.009 -.080463 -.0117403
cpi | -.021785 .0121113 -1.80 0.072 -.0455228 .0019527
Nigeria | 3.033168 .5995214 5.06 0.000 1.858128 4.208208
SriLanka | .2917484 .5777481 0.50 0.614 -.8406169 1.424114
Bangladesh | .2904912 .5843622 0.50 0.619 -.8548377 1.43582
India | -.5742879 .5308723 -1.08 0.279 -1.614779 .4662027
Kenya | 2.126572 .5044502 4.22 0.000 1.137868 3.115277
Malaysia | -2.424813 .6493439 -3.73 0.000 -3.697504 -1.152122
Egypt | -1.831614 .6096875 -3.00 0.003 -3.02658 -.6366488
Philippines | -.6361527 .466915 -1.36 0.173 -1.551289 .2789839
China | -1.47036 .8933924 -1.65 0.100 -3.221377 .2806571
Turkey | 1.160753 .6421596 1.81 0.071 -.0978566 2.419363
Thailand | -.3987376 .6466344 -0.62 0.537 -1.666118 .8686425
_cons | 12.48027 1.468979 8.50 0.000 9.601125 15.35942
-------------+----------------------------------------------------------------
sigma_u | .87351738
sigma_e | .63462076
rho | .65452757 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Command # 3:
Code:
reg nim nir cr lta otoi nlta ooiti eata car bm inf pcgg cpi Nigeria SriLanka Bangladesh India Kenya Mal
> aysia Egypt Philippines China Turkey Thailand
Source | SS df MS Number of obs = 413
-------------+------------------------------ F( 23, 389) = 43.16
Model | 1217.92882 23 52.9534271 Prob > F = 0.0000
Residual | 477.245929 389 1.22685329 R-squared = 0.7185
-------------+------------------------------ Adj R-squared = 0.7018
Total | 1695.17475 412 4.11450183 Root MSE = 1.1076
------------------------------------------------------------------------------
nim | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nir | .0244971 .0138858 1.76 0.078 -.0028035 .0517976
cr | -.0002709 .0012601 -0.22 0.830 -.0027484 .0022066
lta | .0197848 .1800724 0.11 0.913 -.3342522 .3738218
otoi | -.0175484 .0053328 -3.29 0.001 -.028033 -.0070637
nlta | .0225154 .0078639 2.86 0.004 .0070543 .0379764
ooiti | -.0438234 .0069521 -6.30 0.000 -.0574918 -.0301551
eata | -.0503858 .0111521 -4.52 0.000 -.0723117 -.0284598
car | .0722298 .0180023 4.01 0.000 .0368357 .1076239
bm | .0161238 .0127652 1.26 0.207 -.0089736 .0412211
inf | .0175698 .0278604 0.63 0.529 -.0372059 .0723456
pcgg | -.0524609 .0293188 -1.79 0.074 -.110104 .0051822
cpi | -.0428361 .0180001 -2.38 0.018 -.0782257 -.0074465
Nigeria | 2.65039 .3263086 8.12 0.000 2.008841 3.291939
SriLanka | .8536365 .3892656 2.19 0.029 .0883088 1.618964
Bangladesh | .8205079 .3720648 2.21 0.028 .0889982 1.552018
India | -.3762995 .3591384 -1.05 0.295 -1.082395 .3297957
Kenya | 2.897395 .3746782 7.73 0.000 2.160747 3.634043
Malaysia | -2.140142 .4942142 -4.33 0.000 -3.111807 -1.168477
Egypt | -.9467589 .311581 -3.04 0.003 -1.559352 -.3341653
Philippines | -.3202755 .2946155 -1.09 0.278 -.8995135 .2589625
China | -1.556073 .5554704 -2.80 0.005 -2.648172 -.4639728
Turkey | 1.475286 .4628376 3.19 0.002 .5653102 2.385263
Thailand | -1.002632 .4558766 -2.20 0.028 -1.898923 -.1063418
_cons | 9.64989 1.527788 6.32 0.000 6.646134 12.65365
------------------------------------------------------------------------------
.
Confusion with the second command is that it uses xtreg plus it doesn't use -fe- option.
I read LSDV is simply OLS applied on model that includes country dummies. So, is -xtreg- the right command or is the 3rd command that uses -reg- correct?'
Please reply! Which among these three commands actually perform LSDV in my model?