请教高手：怎样把Frontier 4.1 计算出的技术效率输入spss 啊？

把Frontier 4.1 计算出的技术效率从记事本复制到excel 里时遇到了麻烦，前面的序号总是和计算出来的技术效率在同一个单元格里出现，如果把序号删掉的话技术效率也被删掉了；

复制到spss 里时根本就没有数据显示出来；请问怎样才能把技术效率输入excel 或 spss 、 stata 之类的软件啊？谢谢！

不知道我理解的对不对。

输入输出数据的成倍增长不能说明效率就会保持不变。应该是在一定的最佳效率下得到最佳的输入与输出组合，这样的组合未必就是按照比例变化的。

软件应该可以在*。IN中自动算出对数形式的，你这样做，是不是对数了2遍啊？除非你在INS中不要在对数化了，敲入“N”

Panel data 的格式怎么输入啊？比如我是5年9个地区的数据，那么我输入后怎么不执行呢？我错在哪里了？请多多赐教啊？

序列号 时期 产出 投入1 投入2

1 1 6.963256209 14.2902293 7.246539127
2 1 6.959939676 14.28643127 7.247073462
3 1 7.00475494 14.28495991 7.248390268
4 1 7.060673771 14.28058996 7.246938124
5 1 7.194639533 14.28233251 7.247707169
1 2 3.282211122 10.5754877 3.60359386
2 2 3.271172216 10.57390332 3.60359386
3 2 3.267745986 10.57128359 3.60359386
4 2 3.316260216 10.57128359 3.60359386
5 2 3.416959176 10.57150409 3.60359386
1 3 2.592788983 10.35443578 3.444256871
2 3 2.608598122 10.35472239 3.444256871
3 3 2.617768026 10.35111775 3.443618098
4 3 2.63094595 10.34844577 3.440738556
5 3 2.790741705 10.34620349 3.438493166
1 4 3.402766151 10.68434795 3.274121299
2 4 3.461727881 10.67929708 3.662022
3 4 3.43419686 10.67557992 3.661251287
4 4 3.483078399 10.67563074 3.660994251
5 4 3.630935143 10.67593563 3.663561646
1 5 3.028451126 10.28370323 3.208421367
2 5 2.949745925 10.2856158 3.20720802
3 5 2.949761631 10.29077511 3.20720802
4 5 2.99326423 10.31463316 3.20720802
5 5 3.10212176 10.32365735 3.20720802
1 6 2.329129648 10.05406041 3.048324724
2 6 2.344897177 10.04407524 3.047375507
3 6 2.34999335 10.01677159 3.04594999
4 6 2.416047698 9.991731919 3.043569603
5 6 2.578336275 9.985588141 3.042138646
1 7 1.642853346 9.333530983 2.32238772
2 7 1.735964831 9.353487881 2.341805806
3 7 1.538371464 9.403148419 2.351375257
4 7 1.638724833 9.411450971 2.361796953
5 7 1.807156983 9.414431574 2.371177884
1 8 3.08328059 7.798523054 0.500775288
2 8 3.10568473 7.80588204 0.500775288
3 8 3.119952325 7.783765536 0.500775288
4 8 3.136302585 7.790530592 0.500775288
5 8 3.238898036 7.791109511 0.500775288
1 9 3.921014461 10.40843594 3.395514599
2 9 3.921666358 10.40870749 3.391483689
3 9 3.922525659 10.3993782 3.368329778
4 9 3.923508791 10.3991041 3.380314171
5 9 3.959137481 10.39910715 3.629925311

或者输入这样的面板数据格式（try.dat)，可也不行啊

序列号 时期列 产出 投入1 投入2

1.00 1.00 6.96 14.29 7.25
2.00 1.00 6.96 14.29 7.25
3.00 1.00 7.00 14.28 7.25
4.00 1.00 7.06 14.28 7.25
5.00 1.00 7.19 14.28 7.25
6.00 2.00 3.28 10.58 3.60
7.00 2.00 3.27 10.57 3.60
8.00 2.00 3.27 10.57 3.60
9.00 2.00 3.32 10.57 3.60
10.00 2.00 3.42 10.57 3.60
11.00 3.00 2.59 10.35 3.44
12.00 3.00 2.61 10.35 3.44
13.00 3.00 2.62 10.35 3.44
14.00 3.00 2.63 10.35 3.44
15.00 3.00 2.79 10.35 3.44
16.00 4.00 3.40 10.68 3.27
17.00 4.00 3.46 10.68 3.66
18.00 4.00 3.43 10.68 3.66
19.00 4.00 3.48 10.68 3.66
20.00 4.00 3.63 10.68 3.66
21.00 5.00 3.03 10.28 3.21
22.00 5.00 2.95 10.29 3.21
23.00 5.00 2.95 10.29 3.21
24.00 5.00 2.99 10.31 3.21
25.00 5.00 3.10 10.32 3.21
26.00 6.00 2.33 10.05 3.05
27.00 6.00 2.34 10.04 3.05
28.00 6.00 2.35 10.02 3.05
29.00 6.00 2.42 9.99 3.04
30.00 6.00 2.58 9.99 3.04
31.00 7.00 1.64 9.33 2.32
32.00 7.00 1.74 9.35 2.34
33.00 7.00 1.54 9.40 2.35
34.00 7.00 1.64 9.41 2.36
35.00 7.00 1.81 9.41 2.37
36.00 8.00 3.08 7.80 0.50
37.00 8.00 3.11 7.81 0.50
38.00 8.00 3.12 7.78 0.50
39.00 8.00 3.14 7.79 0.50
40.00 8.00 3.24 7.79 0.50
41.00 9.00 3.92 10.41 3.40
42.00 9.00 3.92 10.41 3.39
43.00 9.00 3.92 10.40 3.37
44.00 9.00 3.92 10.40 3.38
45.00 9.00 3.96 10.40 3.63

predict tei,te

就可以了，不要搞得这么复杂，不过做面板随机前沿的时候好象不太好

有两个选项，一个是时间不变，一个是随时间消减

step1.利用wordpad編輯eg1.dat

如下 比數 期數 Q L K

1.000000 1.000000 2.547725 2.242410 3.559169
2.000000 1.000000 3.189859 1.535361 4.347655
3.000000 1.000000 3.037594 1.628260 4.497574
4.000000 1.000000 2.581201 1.596353 3.575095
5.000000 1.000000 2.486406 2.165275 3.327838
6.000000 1.000000 2.726807 0.6391333E-01 4.523678
7.000000 1.000000 1.903152 -1.354796 4.584018
8.000000 1.000000 3.075283 1.845932 4.407743
9.000000 1.000000 2.679239 0.8544153 3.660377
10.00000 1.000000 2.620530 0.7325046E-01 4.403800
11.00000 1.000000 1.333157 1.233143 2.248762
12.00000 1.000000 3.614102 1.394511 4.009077
13.00000 1.000000 2.419034 2.076312 4.292239
14.00000 1.000000 2.223217 2.028674 3.192532
15.00000 1.000000 2.536392 -1.067114 4.180216
16.00000 1.000000 2.269752 0.8919980 4.156364
17.00000 1.000000 2.907720 2.065723 4.081614
18.00000 1.000000 2.838728 1.066778 4.284607
19.00000 1.000000 3.071118 0.9813291 4.232888
20.00000 1.000000 2.937679 1.439835 4.050462
21.00000 1.000000 2.839195 0.9787020 4.475551
22.00000 1.000000 2.677591 0.8981268 3.427157
23.00000 1.000000 3.179345 1.039216 4.540461
24.00000 1.000000 1.915009 -0.8232559 3.582435
25.00000 1.000000 1.854421 -1.164752 4.546099
26.00000 1.000000 3.496386 1.183260 4.561981
27.00000 1.000000 3.259634 1.909839 4.385458
28.00000 1.000000 2.810547 1.487270 3.909740
29.00000 1.000000 2.309064 0.4593218 3.094310
30.00000 1.000000 1.880076 -0.9761283E-01 3.656537
31.00000 1.000000 1.263844 1.816289 1.671849
32.00000 1.000000 0.5068176 -0.7360547 0.9242589
33.00000 1.000000 2.819890 0.6703902 3.726777
34.00000 1.000000 2.311049 2.100347 4.225212
35.00000 1.000000 1.955011 1.399951 4.351000
36.00000 1.000000 3.459246 1.615221 4.354296
37.00000 1.000000 3.475160 1.936869 4.594150
38.00000 1.000000 2.679033 2.128827 3.755135
39.00000 1.000000 2.506179 0.9174900 4.090019
40.00000 1.000000 2.540263 1.885553 2.895083
41.00000 1.000000 2.555676 1.966972 3.282827
42.00000 1.000000 2.762602 2.084429 3.921736
43.00000 1.000000 3.203681 1.588419 4.479403
44.00000 1.000000 3.023882 1.905386 3.416119
45.00000 1.000000 3.054378 1.433893 4.562618
46.00000 1.000000 2.525168 1.515347 3.602886
47.00000 1.000000 3.475470 1.977270 4.492136
48.00000 1.000000 2.745667 2.257692 3.369191
49.00000 1.000000 2.596299 1.583299 3.912383
50.00000 1.000000 2.325129 2.231304 3.713475
51.00000 1.000000 2.992076 1.062985 4.143944
52.00000 1.000000 3.293538 2.090010 4.110743
53.00000 1.000000 3.578646 2.158253 4.544985
54.00000 1.000000 2.546942 1.231685 3.671530
55.00000 1.000000 2.913003 0.6512830 4.364728
56.00000 1.000000 2.988355 1.820833 4.164756
57.00000 1.000000 1.184484 1.972413 0.7045846E-01
58.00000 1.000000 3.061426 2.233128 4.467332
59.00000 1.000000 3.300419 2.058473 4.099995
60.00000 1.000000 2.646529 1.726510 3.789132

step2.利用wordpad編輯eg1.pro(記住eg1.pro與eg1.dat與eg1.out 要在同一子目錄)

1 1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
eg1.dat DATA FILE NAME
eg1.out OUTPUT FILE NAME
1 1=PRODUCTION FUNCTION, 2=COST FUNCTION
y LOGGED DEPENDENT VARIABLE (Y/N)
60 NUMBER OF CROSS-SECTIONS
1 NUMBER OF TIME PERIODS
60 NUMBER OF OBSERVATIONS IN TOTAL
2 NUMBER OF REGRESSOR VARIABLES (Xs)
n MU (Y/N) [OR DELTA0 (Y/N) IF USING TE EFFECTS MODEL]
n ETA (Y/N) [OR NUMBER OF TE EFFECTS REGRESSORS (Zs)]
n STARTING VALUES (Y/N)
IF YES THEN BETA0
BETA1 TO
BETAK
SIGMA SQUARED
GAMMA
MU [OR DELTA0
ETA DELTA1 TO
DELTAP]

NOTE: IF YOU ARE SUPPLYING STARTING VALUES
AND YOU HAVE RESTRICTED MU [OR DELTA0] TO BE
ZERO THEN YOU SHOULD NOT SUPPLY A STARTING
VALUE FOR THIS PARAMETER.

step3. 在sfa(目錄可以變更)按執行檔二下

1.因為你有eg1.pro程式檔所以先按 f

2.再輸入 eg1.pro

3.sfa目錄會產生 eg1.out 可利用wordpad 打開

Output from the program FRONTIER (Version 4.1)

instruction file = eg1.ins
data file = eg1.dta

Error Components Frontier (see B&C 1992)
The model is a production function
The dependent variable is logged

the ols estimates are :

coefficient standard-error t-ratio

beta 0 0.24489834E+00 0.21360307E+00 0.11465114E+01
beta 1 0.28049246E+00 0.48066617E-01 0.58354940E+01
beta 2 0.53330637E+00 0.51498586E-01 0.10355748E+02
sigma-squared 0.11398496E+00

log likelihood function = -0.18446849E+02

the estimates after the grid search were :

beta 0 0.58014216E+00
beta 1 0.28049246E+00
beta 2 0.53330637E+00
sigma-squared 0.22067413E+00
gamma 0.80000000E+00
mu is restricted to be zero
eta is restricted to be zero

iteration = 0 func evals = 19 llf = -0.17034854E+02
0.58014216E+00 0.28049246E+00 0.53330637E+00 0.22067413E+00 0.80000000E+00
gradient step
iteration = 5 func evals = 41 llf = -0.17027230E+02
0.56160697E+00 0.28108701E+00 0.53647803E+00 0.21694170E+00 0.79718731E+00
iteration = 7 func evals = 63 llf = -0.17027229E+02
0.56161963E+00 0.28110205E+00 0.53647981E+00 0.21700046E+00 0.79720730E+00

the final mle estimates are :

coefficient standard-error t-ratio

beta 0 0.56161963E+00 0.20261668E+00 0.27718331E+01
beta 1 0.28110205E+00 0.47643365E-01 0.59001301E+01
beta 2 0.53647981E+00 0.45251553E-01 0.11855501E+02
sigma-squared 0.21700046E+00 0.63909106E-01 0.33954545E+01
gamma 0.79720730E+00 0.13642399E+00 0.58436004E+01
mu is restricted to be zero
eta is restricted to be zero

log likelihood function = -0.17027229E+02

LR test of the one-sided error = 0.28392402E+01
with number of restrictions = 1
[note that this statistic has a mixed chi-square distribution]

number of iterations = 7

(maximum number of iterations set at : 100)

number of cross-sections = 60

number of time periods = 1

total number of observations = 60

thus there are: 0 obsns not in the panel

covariance matrix :

0.41053521E-01 -0.31446721E-02 -0.80030279E-02 0.40456494E-02 0.92519362E-02
-0.31446721E-02 0.22698902E-02 0.40106205E-04 -0.29528845E-04 -0.91550467E-04
-0.80030279E-02 0.40106205E-04 0.20477030E-02 -0.47190308E-04 -0.16404645E-03
0.40456494E-02 -0.29528845E-04 -0.47190308E-04 0.40843738E-02 0.67450773E-02
0.92519362E-02 -0.91550467E-04 -0.16404645E-03 0.67450773E-02 0.18611506E-01

technical efficiency estimates :

firm eff.-est.

1 0.65068880E+00
2 0.82889151E+00
3 0.72642592E+00
4 0.74785113E+00
5 0.69133584E+00
6 0.77654637E+00
7 0.56516787E+00
8 0.73768185E+00
9 0.84388964E+00
10 0.75784167E+00
11 0.54558432E+00
12 0.93739520E+00
13 0.44809682E+00
14 0.61831027E+00
15 0.87384359E+00
16 0.54952777E+00
17 0.71262499E+00
18 0.75907226E+00
19 0.85727198E+00
20 0.80651927E+00
21 0.72458613E+00
22 0.87223606E+00
23 0.83681369E+00
24 0.75225715E+00
25 0.52974774E+00
26 0.89731683E+00
27 0.81013415E+00
28 0.78179413E+00
29 0.85610585E+00
30 0.62097885E+00
31 0.57938181E+00
32 0.74934194E+00
33 0.88192581E+00
34 0.42082174E+00
35 0.35126244E+00
36 0.88908382E+00
37 0.84118609E+00
38 0.67868899E+00
39 0.67291047E+00
40 0.83853427E+00
41 0.75964587E+00
42 0.68189614E+00
43 0.80438742E+00
44 0.88652992E+00
45 0.74299265E+00
46 0.72610191E+00
47 0.85341515E+00
48 0.78519185E+00
49 0.67207111E+00
50 0.51430249E+00
51 0.84238134E+00
52 0.85098581E+00
53 0.85963850E+00
54 0.75508293E+00
55 0.81649829E+00
56 0.75991250E+00
57 0.87350729E+00
58 0.66471456E+00
59 0.85670448E+00
60 0.70842786E+00

mean efficiency = 0.74056772E+00

嗬嗬，一年多了，这个帖子又热闹了。

回答一个简单问题，Frontier的结果拷到EXcel或Spss是文本格式，可以先考到Word里边，然后将文本转化成表格，再拷到EXcel或Spss就可以了。

其他的问题慢慢来，可能需要把原理整明白，否则照猫画虎，总是很容易出问题。嗬嗬，语气有点过分，但这是我的经验教训，不能有侥幸心理啊。

各位对SFA感兴趣的朋友:

大家过年好!谢谢楼主开辟此讨论空间,关于SFA的使用我还有很多地方不明白,自己找了一些文献拿出来与大家分享,同时也希望哪位大侠有SFA的经典文献和重要文献,能否挂出来或发给在下,感谢万分! forddong@126.com

太大了，只能分两次上传拉

参数就是final项上的值,重要的是一个截断正态分布的mu值要算一下

还有就是各个项的加减处理

请问使用超越对数模型估计成本前沿函数怎么操作呢，是不是各交叉项数据都要在excel中生成啊？谢谢指教，不胜感激

你有没有好好看他的使用说明啊　　很简单的啊

我在stata中用xtfrontier做SFA，可总是出现no concave，不收敛，是什么原因呢，请问如何解决？

看看

我的数据共有21年,怎么效率只有一个平均数:

Output from the program FRONTIER (Version 4.1c)

instruction file = eg5.ins
data file = eg5.dta

Error Components Frontier (see B&C 1992)
The model is a production function
The dependent variable is logged

the ols estimates are :

coefficient standard-error t-ratio

beta 0 0.99745945E-01 0.10620374E+00 0.93919427E+00
beta 1 -0.64825893E+00 0.14746607E+00 -0.43959871E+01
beta 2 0.60824725E+00 0.14050029E+00 0.43291529E+01
beta 3 0.10015320E+01 0.13863712E+00 0.72241257E+01
beta 4 0.16406926E+00 0.77600612E-01 0.21142779E+01
beta 5 0.14576679E+00 0.59936948E-01 0.24320023E+01
beta 6 0.29744038E+00 0.70302576E-01 0.42308604E+01
beta 7 0.20773723E+00 0.11837857E+00 0.17548550E+01
beta 8 -0.35329800E+00 0.91020767E-01 -0.38815098E+01
beta 9 -0.45870499E+00 0.11840406E+00 -0.38740646E+01
sigma-squared 0.45132464E-01

log likelihood function = 0.97563772E+02

the estimates after the grid search were :

beta 0 0.27466270E+00
beta 1 -0.64825893E+00
beta 2 0.60824725E+00
beta 3 0.10015320E+01
beta 4 0.16406926E+00
beta 5 0.14576679E+00
beta 6 0.29744038E+00
beta 7 0.20773723E+00
beta 8 -0.35329800E+00
beta 9 -0.45870499E+00
sigma-squared 0.75093560E-01
gamma 0.64000000E+00
mu 0.00000000E+00
eta is restricted to be zero


iteration = 0 func evals = 20 llf = 0.21384538E+03
0.27466270E+00-0.64825893E+00 0.60824725E+00 0.10015320E+01 0.16406926E+00
0.14576679E+00 0.29744038E+00 0.20773723E+00-0.35329800E+00-0.45870499E+00
0.75093560E-01 0.64000000E+00 0.00000000E+00
gradient step
iteration = 5 func evals = 38 llf = 0.29846279E+03
0.22076183E+00-0.67088598E+00 0.61148128E+00 0.98914711E+00 0.15298385E+00
0.22287127E+00 0.25667882E+00 0.23695420E+00-0.29088204E+00-0.47307744E+00
0.99458069E-01 0.81371055E+00 0.13010753E+00
pt better than entering pt cannot be found
iteration = 9 func evals = 56 llf = 0.33416580E+03
0.21690950E+00-0.63528166E+00 0.70689744E+00 0.10682844E+01-0.48658422E-01
0.30880651E+00 0.17862915E+00 0.20859223E+00 0.53281518E-01-0.64921898E+00
0.78591182E-01 0.80067679E+00 0.50170165E+00

the final mle estimates are :

coefficient standard-error t-ratio

beta 0 0.21690950E+00 0.81023331E+00 0.26771240E+00
beta 1 -0.63528166E+00 0.73167936E+00 -0.86825143E+00
beta 2 0.70689744E+00 0.84701810E+00 0.83457183E+00
beta 3 0.10682844E+01 0.75261565E+00 0.14194289E+01
beta 4 -0.48658422E-01 0.47312766E+00 -0.10284417E+00
beta 5 0.30880651E+00 0.32212919E+00 0.95864180E+00
beta 6 0.17862915E+00 0.42953786E+00 0.41586356E+00
beta 7 0.20859223E+00 0.69694261E+00 0.29929614E+00
beta 8 0.53281518E-01 0.56998099E+00 0.93479465E-01
beta 9 -0.64921898E+00 0.66964541E+00 -0.96949664E+00
sigma-squared 0.78591182E-01 0.60433087E-01 0.13004661E+01
gamma 0.80067679E+00 0.20282970E+00 0.39475323E+01
mu 0.50170165E+00 0.87042395E+00 0.57638768E+00
eta is restricted to be zero

log likelihood function = 0.33416580E+03

LR test of the one-sided error = 0.47320405E+03
with number of restrictions = 2
[note that this statistic has a mixed chi-square distribution]

number of iterations = 9

(maximum number of iterations set at : 100)

number of cross-sections = 34

number of time periods = 21

total number of observations = 711

thus there are: 3 obsns not in the panel

covariance matrix :

0.65647801E+00 -0.21330043E+00 -0.22869432E+00 -0.16328816E+00 -0.78325435E-02
0.10138394E+00 -0.14017767E-01 -0.13546633E-01 0.17372959E+00 -0.68137961E-01
-0.42193903E-02 0.16804600E-01 0.23796757E+00
-0.21330043E+00 0.53535469E+00 -0.16677325E+00 -0.84227590E-01 -0.25584855E+00
-0.13882225E+00 -0.10050070E+00 0.16465122E+00 0.59970037E-01 0.19724724E+00
0.59564747E-03 0.90953031E-02 0.27863951E-01
-0.22869432E+00 -0.16677325E+00 0.71743966E+00 -0.27612865E+00 0.82141709E-01
-0.11506954E+00 0.81951856E-01 -0.75955387E-01 -0.32393176E-01 -0.19324685E-01
-0.14335306E-01 -0.53476543E-01 0.97270422E-01
-0.16328816E+00 -0.84227590E-01 -0.27612865E+00 0.56643031E+00 0.19409665E+00
0.52181740E-01 -0.13052458E-01 -0.40606867E-01 -0.30205202E+00 0.38210095E-01
-0.83245364E-02 -0.54747774E-01 0.15708285E-01
-0.78325435E-02 -0.25584855E+00 0.82141709E-01 0.19409665E+00 0.22384978E+00
0.49821364E-01 -0.32042697E-01 -0.21830584E+00 -0.89823304E-01 0.51535577E-01
-0.60369805E-02 -0.34037784E-01 0.17879561E-01
0.10138394E+00 -0.13882225E+00 -0.11506954E+00 0.52181740E-01 0.49821364E-01
0.10376722E+00 0.25011367E-01 -0.55376886E-01 0.39318313E-01 -0.10473952E+00
0.11961046E-01 0.38611528E-01 -0.15356082E+00
-0.14017767E-01 -0.10050070E+00 0.81951856E-01 -0.13052458E-01 -0.32042697E-01
0.25011367E-01 0.18450278E+00 0.21359715E+00 -0.11149241E+00 -0.26823116E+00
0.66795154E-03 0.64534227E-03 -0.24098153E-01
-0.13546633E-01 0.16465122E+00 -0.75955387E-01 -0.40606867E-01 -0.21830584E+00
-0.55376886E-01 0.21359715E+00 0.48572900E+00 -0.15757949E+00 -0.27548443E+00
-0.59035797E-02 -0.11823186E-01 0.11787387E+00
0.17372959E+00 0.59970037E-01 -0.32393176E-01 -0.30205202E+00 -0.89823304E-01
0.39318313E-01 -0.11149241E+00 -0.15757949E+00 0.32487833E+00 0.81187054E-01
0.18676665E-01 0.79789897E-01 -0.17048879E+00
-0.68137961E-01 0.19724724E+00 -0.19324685E-01 0.38210095E-01 0.51535577E-01
-0.10473952E+00 -0.26823116E+00 -0.27548443E+00 0.81187054E-01 0.44842498E+00
-0.12198946E-01 -0.41319945E-01 0.16403142E+00
-0.42193903E-02 0.59564747E-03 -0.14335306E-01 -0.83245364E-02 -0.60369805E-02
0.11961046E-01 0.66795154E-03 -0.59035797E-02 0.18676665E-01 -0.12198946E-01
0.36521580E-02 0.11826261E-01 -0.49468354E-01
0.16804600E-01 0.90953031E-02 -0.53476543E-01 -0.54747774E-01 -0.34037784E-01
0.38611528E-01 0.64534227E-03 -0.11823186E-01 0.79789897E-01 -0.41319945E-01
0.11826261E-01 0.41139886E-01 -0.14707029E+00
0.23796757E+00 0.27863951E-01 0.97270422E-01 0.15708285E-01 0.17879561E-01
-0.15356082E+00 -0.24098153E-01 0.11787387E+00 -0.17048879E+00 0.16403142E+00
-0.49468354E-01 -0.14707029E+00 0.75763785E+00

technical efficiency estimates :

firm eff.-est.

1 0.39797308E+00
2 0.43950143E+00
3 0.71611463E+00
4 0.58881482E+00
5 0.56729586E+00
6 0.75974827E+00
7 0.35302153E+00
8 0.38576381E+00
9 0.49681871E+00
10 0.87716396E+00
11 0.38988264E+00
12 0.57522350E+00
13 0.58247577E+00
14 0.54239556E+00
15 0.63513122E+00
16 0.43704137E+00
17 0.56333835E+00
18 0.63642830E+00
19 0.24327914E+00
20 0.38939029E+00
21 0.35778001E+00
22 0.53050236E+00
23 0.37264113E+00
24 0.54441948E+00
25 0.45205283E+00
26 0.39300706E+00
27 0.33014916E+00
28 0.38637172E+00
29 0.50226369E+00
30 0.41534026E+00
31 0.41911366E+00
32 0.47232976E+00
33 0.44505149E+00
34 0.60558569E+00

mean efficiency = 0.49421796E+00

summary of panel of observations:
(1 = observed, 0 = not observed)

t: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
n
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
17 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
21 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
22 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
24 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
25 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
26 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
27 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
28 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
29 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
30 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
31 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21
32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 20
33 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 20
34 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 20

34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 31 711

毕业论文要用的数据处理,哪为高人能帮我用frontier处理下数据啊.急啊!万分感谢啊!

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