Dear, all
I have some questions during my running STATA. I am running a stochastic frontier approach, based on Battese and Coelli's (1995) model, by using sfpanel code(developed by Belotti et al., (2013)).
I have 6 independent variables, time trend (t), and 4 regional dummy variables in the stochastic frontier. As for the inefficiency function, I used 4 explanatory variables and 2 dummy variables.
I made data scaled and cleaned, so that I could obtain the result as followed:
However, something interesting is that when I run the code, by changing the order of exogenous variables (see below the command "emean"), the result comes out differently with NAs.
Here is one of the examples as followed:
I wonder how the results are different if I changed the order of variables.
Is there something that I've missed? or something wrong?
I have some questions during my running STATA. I am running a stochastic frontier approach, based on Battese and Coelli's (1995) model, by using sfpanel code(developed by Belotti et al., (2013)).
I have 6 independent variables, time trend (t), and 4 regional dummy variables in the stochastic frontier. As for the inefficiency function, I used 4 explanatory variables and 2 dummy variables.
I made data scaled and cleaned, so that I could obtain the result as followed:
Code:
sfpanel $output $labor $land $fert $capital $climate_0 $dum t $dummy, model(bc95) d(t) emean(export_fix urb_ratio mean_sch gdp_corr flo_dum1 drought_dum1)
Code:
Inefficiency effects model (truncated-normal) Number of obs = 609
Group variable: id Number of groups = 31
Time variable: year Obs per group: min = 13
avg = 19.6
max = 20
Prob > chi2 = 0.0000
Log likelihood = -155.2237 Wald chi2(11) = 5580.29
------------------------------------------------------------------------------
lnvalue_agr | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Frontier |
lnlabor | .6297881 .0248619 25.33 0.000 .5810598 .6785165
lnland | .1455606 .0340051 4.28 0.000 .0789117 .2122094
lnfert | .0865237 .0089076 9.71 0.000 .0690651 .1039823
lncapital | .1419817 .0183148 7.75 0.000 .1060855 .177878
temp | -.0017632 .0097441 -0.18 0.856 -.0208614 .0173349
prcp | .0005432 .0000461 11.79 0.000 .000453 .0006335
dum2 | .1528198 .0635501 2.40 0.016 .0282638 .2773758
dum3 | 1.893081 .0909385 20.82 0.000 1.714845 2.071318
dum4 | 1.850183 .1083842 17.07 0.000 1.637754 2.062612
dum5 | .4453163 .0715977 6.22 0.000 .3049874 .5856451
t | .0169506 .0023294 7.28 0.000 .012385 .0215162
_cons | 2.837632 .3713127 7.64 0.000 2.109872 3.565391
-------------+----------------------------------------------------------------
Mu |
export_fix | -2.636783 .4892806 -5.39 0.000 -3.595755 -1.677811
urb_ratio | -3.725086 .5167533 -7.21 0.000 -4.737904 -2.712268
mean_sch | .4050751 .0432029 9.38 0.000 .320399 .4897512
gdp_corr | .3778436 .0723724 5.22 0.000 .2359963 .519691
flo_dum1 | .0371779 .0536684 0.69 0.488 -.0680103 .1423661
drought_dum1 | .0419306 .0659389 0.64 0.525 -.0873074 .1711686
_cons | -1.005427 .1505207 -6.68 0.000 -1.300442 -.7104116
-------------+----------------------------------------------------------------
Usigma |
_cons | -5.386039 1.25924 -4.28 0.000 -7.854104 -2.917974
-------------+----------------------------------------------------------------
Vsigma |
_cons | -2.340532 .0602993 -38.82 0.000 -2.458716 -2.222347
-------------+----------------------------------------------------------------
sigma_u | .0676763 .0426103 1.59 0.112 .0197017 .2324717
sigma_v | .3102844 .009355 33.17 0.000 .2924802 .3291724
lambda | .2181105 .0459056 4.75 0.000 .1281372 .3080838
------------------------------------------------------------------------------
Here is one of the examples as followed:
Code:
sfpanel $output $labor $land $fert $capital $climate_0 $dum t $dummy, model(bc95) d(t) emean(urb_ratio export_fix mean_sch gdp_corr flo_dum1 drought_dum1)
Code:
Inefficiency effects model (truncated-normal) Number of obs = 609
Group variable: id Number of groups = 31
Time variable: year Obs per group: min = 13
avg = 19.6
max = 20
Prob > chi2 = 0.0000
Log likelihood = -170.1489 Wald chi2(11) = 5347.07
------------------------------------------------------------------------------
lnvalue_agr | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Frontier |
lnlabor | .6136596 .0253306 24.23 0.000 .5640125 .6633067
lnland | .1913424 .0362745 5.27 0.000 .1202457 .2624391
lnfert | .0979961 .0092775 10.56 0.000 .0798126 .1161796
lncapital | .1288264 .019428 6.63 0.000 .0907482 .1669046
temp | -.014941 .0101247 -1.48 0.140 -.0347851 .0049032
prcp | .0006067 .0000501 12.11 0.000 .0005086 .0007049
dum2 | .1929125 .0638062 3.02 0.002 .0678545 .3179704
dum3 | 1.885857 .097887 19.27 0.000 1.694002 2.077712
dum4 | 1.689854 .1175275 14.38 0.000 1.459504 1.920204
dum5 | .4981003 .0717566 6.94 0.000 .35746 .6387406
t | .0139226 .0024617 5.66 0.000 .0090978 .0187474
_cons | 2.968205 .3810191 7.79 0.000 2.221422 3.714989
-------------+----------------------------------------------------------------
Mu |
urb_ratio | -3.521951 .6684924 -5.27 0.000 -4.832172 -2.21173
export_fix | -3.473571 . . . . .
mean_sch | .4193095 .0521165 8.05 0.000 .3171631 .521456
gdp_corr | .3108142 .10723 2.90 0.004 .1006473 .5209811
flo_dum1 | .0693675 .0751694 0.92 0.356 -.0779617 .2166968
drought_dum1 | .0505226 .0909306 0.56 0.578 -.1276981 .2287433
_cons | -1.111038 .2249489 -4.94 0.000 -1.551929 -.6701459
-------------+----------------------------------------------------------------
Usigma |
_cons | -3.888564 . . . . .
-------------+----------------------------------------------------------------
Vsigma |
_cons | -2.294794 .0645472 -35.55 0.000 -2.421305 -2.168284
-------------+----------------------------------------------------------------
sigma_u | .1430899 . . . . .
sigma_v | .317462 .0102456 30.99 0.000 .2980028 .3381918
lambda | .4507308 . . . . .
------------------------------------------------------------------------------
Is there something that I've missed? or something wrong?
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