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| From | Scott Merryman <scott.merryman@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: SFE technical estimates STAT and SAS |
| Date | Wed, 9 May 2012 15:41:01 -0500 |
Yes. Using the greene9 data set:
. webuse greene9,clear
. frontier lnv lnk lnl, nolog
Stoc. frontier normal/half-normal model Number of obs = 25
Wald chi2(2) = 743.71
Log likelihood = 2.4695222 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
lnv | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnk | .2585478 .098764 2.62 0.009 .0649738 .4521218
lnl | .7802451 .1199399 6.51 0.000 .5451672 1.015323
_cons | 2.081135 .281641 7.39 0.000 1.529128 2.633141
-------------+----------------------------------------------------------------
/lnsig2v | -3.48401 .6195353 -5.62 0.000 -4.698277 -2.269743
/lnsig2u | -3.014599 1.11694 -2.70 0.007 -5.203761 -.8254368
-------------+----------------------------------------------------------------
sigma_v | .1751688 .0542616 .0954514 .3214633
sigma_u | .2215073 .1237052 .074134 .6618486
sigma2 | .0797496 .0426989 -.0039388 .163438
lambda | 1.264536 .1678684 .9355204 1.593552
------------------------------------------------------------------------------
Likelihood-ratio test of sigma_u=0: chibar2(01) = 0.43 Prob>=chibar2 = 0.256
. predict te, te
. cl state te
state te
1. Alabama .8231754
2. California .8692654
3. Connecticut .8318406
4. Florida .6016339
5. Georgia .9040509
6. Illinois .8891712
7. Indiana .8150898
8. Iowa .785352
9. Kansas .9066431
10. Kentucky .9464481
11. Louisiana .8214222
12. Maine .8061242
13. Maryland .8772239
14. Massachusetts .8596337
15. Michigan .8582037
16. Missouri .9050045
17. NewJersey .9111356
18. NewYork .7636376
19. Ohio .8010177
20. Pennsylvania .8648741
21. Texas .8217006
22. Virginia .8732988
23. Washington .898432
24. WestVirginia .8602608
25. Wisconsin .8727364
From SAS:
proc qlim data=greene covest=hessian;
model lnv = lnk lnl;
endogenous lnv ~ frontier (type=HALF production);
output out = work_out1 TE1 ;
run;
proc print data = work_out1;
var state te1;
run;
Parameter Estimates
Standard Approx
Parameter DF Estimate Error t Value Pr > |t|
Intercept 1 2.081135 0.281648 7.39 <.0001
lnk 1 0.258548 0.098766 2.62 0.0089
lnl 1 0.780245 0.119945 6.51 <.0001
_Sigma_v 1 0.175169 0.054265 3.23 0.0012
_Sigma_u 1 0.221507 0.123706 1.79 0.0734
Obs state TE1
1 Alabama 0.82318
2 California 0.86927
3 Connecticut 0.83184
4 Florida 0.60163
5 Georgia 0.90405
6 Illinois 0.88917
7 Indiana 0.81509
8 Iowa 0.78535
9 Kansas 0.90664
10 Kentucky 0.94645
11 Louisiana 0.82142
12 Maine 0.80612
13 Maryland 0.87722
14 Massachusetts 0.85963
15 Michigan 0.85820
16 Missouri 0.90500
17 NewJersey 0.91114
18 NewYork 0.76364
19 Ohio 0.80102
20 Pennsylvania 0.86487
21 Texas 0.82170
22 Virginia 0.87330
23 Washington 0.89843
24 WestVirginia 0.86026
25 Wisconsin 0.87274
On Wed, May 9, 2012 at 2:53 PM, Price, Joseph <jprice@bus.olemiss.edu> wrote:
> I am running a half-normal production frontier analysis with frontier.
> frontier lnAAA lnBBB lnCCC lnDDD , distribution(hnormal) technique(nr)
> predict ehte, te
> predict ehu, u
> predict ehm, m
>
> I am also running a half-normal production frontier analysis with proc QLIM.
> I would like to be able to get the technical efficiencies to match up to STATA using
> frontier. I have been unable to get the same results from both packages.
>
> qlim data=work_dataset1 METHOD=NEWRAP covest=hessian;
> model lnAAA = lnBBB lnCCC lnDDD;
> endogenous lnAAA ~ frontier (type=HALF production);
> output out = work_out1 TE1 TE2 EXPECTED;
>
> The documentation for the technical efficicneies in SAS are
> TE1
> outputs estimates of
> technical efficiency for each producer in the stochastic frontier model
> suggested by Battese and Coelli (1988).
> TE2
> outputs estimates of
> technical efficiency for each producer in the stochastic frontier model
> suggested by Jondrow et al. (1982).
>
> STATA documents
> produces estimates of the technical efficiency via TE = E(exp(-u|e)]
> This has been documented as derrived from (by Battese and Coelli (1988) too?
> and can also produce
> produces estimates of minus the natural log of the technical efficiency via TE = exp(-E(u|e)]
> produces estimates of minus the natural log of the technical efficiency via TE = exp(-M(u|e)]
>
> I would appreciate any help with this issue. Has anyone been able to get the same results from QLIM and frontier?
>
> *
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