各位大神好，我现在正在做一个关于我国商业银行效率的研究，想用Frontier软件通过SFA方法求各商业银行的效率情况，但是运行结果貌似出了点问题：
1.输出结果显示不能用SFA（这点比较纠结），只能去用OLS;
2.只能输出商业银行成本，而无法输出商业银行利润的结果，即看不到OUT文件。。。；
3.不知道取不取对数是不是必须的，因为去了对数之后却看不到结果文件，不取反而得出了1中的问题。
以下是我引导文件的设置：
1 1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
2014-dta.txt DATA FILE NAME
2014-out.txt OUTPUT FILE NAME
1 1=PRODUCTION FUNCTION, 2=COST FUNCTION
N LOGGED DEPENDENT VARIABLE (Y/N)
16 NUMBER OF CROSS-SECTIONS
7 NUMBER OF TIME PERIODS
112 NUMBER OF OBSERVATIONS IN TOTAL
9 NUMBER OF REGRESSOR VARIABLES (Xs)
Y 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.
输出结果见下图，比较长（因为实在不知道那点重要那点不重要），大神帮忙看完（先谢过了）：
Output from the program FRONTIER (Version 4.1c)
instruction file = 2013-ins.txt
data file = 2014-dta.txt
Error Components Frontier (see B&C 1992)
The model is a production function
The dependent variable is not logged
the ols estimates are :
coefficient standard-error t-ratio
beta 0 0.10886277E+03 0.35636300E+02 0.30548280E+01
beta 1 -0.20819768E-01 0.30557766E-01 -0.68132493E+00
beta 2 -0.49017449E-01 0.44792422E-01 -0.10943246E+01
beta 3 0.29668191E-01 0.33334963E-01 0.89000220E+00
beta 4 0.11975593E-02 0.38157519E-01 0.31384621E-01
beta 5 -0.94709875E-01 0.36883890E-01 -0.25677843E+01
beta 6 0.11174246E+00 0.43760259E-01 0.25535147E+01
beta 7 0.10098689E+00 0.45629712E-01 0.22131827E+01
beta 8 -0.21053054E+01 0.12794095E+02 -0.16455290E+00
beta 9 -0.11095952E+02 0.97632482E+01 -0.11365021E+01
sigma-squared 0.83769011E+04
log likelihood function = -0.65954472E+03
the estimates after the grid search were :
beta 0 0.12470011E+03
beta 1 -0.20819768E-01
beta 2 -0.49017449E-01
beta 3 0.29668191E-01
beta 4 0.11975593E-02
beta 5 -0.94709875E-01
beta 6 0.11174246E+00
beta 7 0.10098689E+00
beta 8 -0.21053054E+01
beta 9 -0.11095952E+02
sigma-squared 0.78797849E+04
gamma 0.50000000E-01
mu is restricted to be zero
eta is restricted to be zero
iteration = 0 func evals = 20 llf = -0.65962456E+03
0.12470011E+03-0.20819768E-01-0.49017449E-01 0.29668191E-01 0.11975593E-02
-0.94709875E-01 0.11174246E+00 0.10098689E+00-0.21053054E+01-0.11095952E+02
0.78797849E+04 0.50000000E-01
gradient step
iteration = 5 func evals = 43 llf = -0.65959110E+03
0.12470009E+03-0.22812302E-01-0.50684934E-01 0.27451665E-01-0.34149587E-03
-0.97152583E-01 0.11072458E+00 0.10049767E+00-0.21053119E+01-0.11095966E+02
0.78797849E+04 0.24304199E-01
iteration = 10 func evals = 67 llf = -0.65958935E+03
0.12469152E+03-0.23139496E-01-0.50377884E-01 0.27386727E-01-0.45767915E-03
-0.96813722E-01 0.11047466E+00 0.99778936E-01-0.21090572E+01-0.11104168E+02
0.78797847E+04 0.23813240E-01
iteration = 15 func evals = 194 llf = -0.65957439E+03
0.11834202E+03-0.21408654E-01-0.50870291E-01 0.28516147E-01 0.17542665E-02
-0.94034267E-01 0.11260917E+00 0.99952844E-01-0.29189480E+01-0.10953189E+02
0.78796023E+04 0.12838088E-01
iteration = 20 func evals = 315 llf = -0.65957328E+03
0.11723275E+03-0.21263352E-01-0.50826407E-01 0.28494539E-01 0.17444373E-02
-0.93912612E-01 0.11241600E+00 0.99900061E-01-0.29845508E+01-0.10993311E+02
0.78795682E+04 0.10000831E-01
iteration = 25 func evals = 439 llf = -0.65957016E+03
0.11538291E+03-0.20812824E-01-0.49016413E-01 0.29675002E-01 0.11911667E-02
-0.94725450E-01 0.11172750E+00 0.10100248E+00-0.21004357E+01-0.11097833E+02
0.78794845E+04 0.84773882E-02
search failed. fn val indep of search direction
iteration = 27 func evals = 462 llf = -0.65957016E+03
0.11537481E+03-0.20813864E-01-0.49023927E-01 0.29670625E-01 0.11955285E-02
-0.94717701E-01 0.11173374E+00 0.10099727E+00-0.21046805E+01-0.11095652E+02
0.78794841E+04 0.84574277E-02
the final mle estimates are :
coefficient standard-error t-ratio
beta 0 0.11537481E+03 0.55059783E+02 0.20954462E+01
beta 1 -0.20813864E-01 0.29228496E-01 -0.71210863E+00
beta 2 -0.49023927E-01 0.43080954E-01 -0.11379490E+01
beta 3 0.29670625E-01 0.32078274E-01 0.92494455E+00
beta 4 0.11955285E-02 0.36740289E-01 0.32539987E-01
beta 5 -0.94717701E-01 0.35438216E-01 -0.26727559E+01
beta 6 0.11173374E+00 0.42082350E-01 0.26551213E+01
beta 7 0.10099727E+00 0.43916647E-01 0.22997491E+01
beta 8 -0.21046805E+01 0.12337641E+02 -0.17059018E+00
beta 9 -0.11095652E+02 0.94172134E+01 -0.11782310E+01
sigma-squared 0.78794841E+04 0.23620068E+01 0.33359278E+04
gamma 0.84574277E-02 0.11260364E+00 0.75107944E-01
mu is restricted to be zero
eta is restricted to be zero
log likelihood function = -0.65957016E+03
the likelihood value is less than that obtained
using ols! - try again using different starting values
number of iterations = 27
(maximum number of iterations set at : 100)
number of cross-sections = 112
number of time periods = 7
total number of observations = 112
thus there are: 672 obsns not in the panel
covariance matrix :
0.30315797E+04 -0.46558437E+00 -0.42678572E+00 -0.48613308E+00 -0.27600525E+00
-0.48566237E+00 -0.23129504E+00 -0.24905257E+00 -0.57910710E+02 -0.81190800E+02
0.11630320E+03 0.48605300E+01
-0.46558437E+00 0.85430497E-03 0.55932767E-04 0.92661433E-04 0.13482663E-03
0.23272191E-05 -0.57939450E-04 -0.11655059E-04 -0.53713150E-02 -0.37886361E-01
-0.13268139E-01 0.19200439E-04
-0.42678572E+00 0.55932767E-04 0.18559686E-02 0.93137050E-04 -0.64122877E-03
-0.46013709E-03 0.29943742E-03 0.61432790E-03 0.26999835E+00 0.19729095E-01
-0.10709833E-01 -0.71875623E-05
-0.48613308E+00 0.92661433E-04 0.93137050E-04 0.10290157E-02 -0.13454730E-03
-0.12166029E-03 -0.29237021E-04 0.13324494E-03 0.11970312E+00 -0.70832601E-01
-0.13130681E-01 0.62473343E-05
-0.27600525E+00 0.13482663E-03 -0.64122877E-03 -0.13454730E-03 0.13498488E-02
0.21771766E-03 -0.24083773E-04 -0.61996302E-03 -0.15335927E+00 -0.23379647E-01
-0.10380932E-01 -0.90381848E-05
-0.48566237E+00 0.23272191E-05 -0.46013709E-03 -0.12166029E-03 0.21771766E-03
0.12558672E-02 0.14821858E-03 0.28236841E-03 -0.19911697E+00 0.11740893E+00
-0.19351743E-01 -0.17258166E-04
-0.23129504E+00 -0.57939450E-04 0.29943742E-03 -0.29237021E-04 -0.24083773E-04
0.14821858E-03 0.17709242E-02 -0.65230876E-03 -0.15766026E+00 0.16920543E-01
-0.11134584E-01 -0.32492842E-04
-0.24905257E+00 -0.11655059E-04 0.61432790E-03 0.13324494E-03 -0.61996302E-03
0.28236841E-03 -0.65230876E-03 0.19286719E-02 0.14083275E+00 -0.80775587E-01
-0.51967685E-02 0.24012312E-04
-0.57910710E+02 -0.53713150E-02 0.26999835E+00 0.11970312E+00 -0.15335927E+00
-0.19911697E+00 -0.15766026E+00 0.14083275E+00 0.15221739E+03 -0.87324868E+01
-0.40570245E+00 0.16759879E-02
-0.81190800E+02 -0.37886361E-01 0.19729095E-01 -0.70832601E-01 -0.23379647E-01
0.11740893E+00 0.16920543E-01 -0.80775587E-01 -0.87324868E+01 0.88683908E+02
-0.46512405E+01 -0.14297382E-02
0.11630320E+03 -0.13268139E-01 -0.10709833E-01 -0.13130681E-01 -0.10380932E-01
-0.19351743E-01 -0.11134584E-01 -0.51967685E-02 -0.40570245E+00 -0.46512405E+01
0.55790761E+01 0.20190446E+00
0.48605300E+01 0.19200439E-04 -0.71875623E-05 0.62473343E-05 -0.90381848E-05
-0.17258166E-04 -0.32492842E-04 0.24012312E-04 0.16759879E-02 -0.14297382E-02
0.20190446E+00 0.12679579E-01
technical efficiency estimates :
firm eff.-est.
1 0.88628324E+00
2 0.90228222E+00
3 0.91075942E+00
4 0.82993642E+00
5 0.40666653E+00
6 0.96413158E+00
7 0.96780152E+00
8 0.92163567E+00
9 0.88639732E+00
10 0.82084969E+00
11 0.85851422E+00
12 0.81882128E+00
13 0.85497737E+00
14 0.97214378E+00
15 0.96291095E+00
16 0.81489837E+00
17 0.92397471E+00
18 0.45390676E+00
19 0.84594284E+00
20 0.76579763E+00
21 0.87801088E+00
22 0.95851523E+00
23 0.97786169E+00
24 0.91391683E+00
25 0.33456623E+00
26 0.95693041E+00
27 0.85192093E+00
28 0.93862443E+00
29 0.90583705E+00
30 0.94320790E+00
31 0.96211939E+00
32 0.70026635E+00
33 0.78163808E+00
34 0.80870589E+00
35 0.84759220E+00
36 0.83065318E+00
37 0.64731107E+00
38 0.97089205E+00
39 0.97161470E+00
40 0.10000000E+01
41 0.10000000E+01
42 0.96273290E+00
43 0.93726746E+00
44 0.84995628E+00
45 0.77531853E+00
46 0.97568320E+00
47 0.96720812E+00
48 0.92178656E+00
49 0.83813667E+00
50 0.78684141E+00
51 0.85321392E+00
52 0.96447813E+00
53 0.87746811E+00
54 0.97277720E+00
55 0.96938091E+00
56 0.10000000E+01
57 0.93340339E+00
58 0.97475057E+00
59 0.92182174E+00
60 0.84862004E+00
61 0.90087674E+00
62 0.96524604E+00
63 0.95646007E+00
64 0.90912975E+00
65 -0.32223479E+00
66 0.93576834E+00
67 0.93173360E+00
68 0.95517995E+00
69 -0.12838097E+01
70 0.97161124E+00
71 0.97437471E+00
72 0.92339992E+00
73 0.95234059E+00
74 0.95652086E+00
75 0.87682668E+00
76 0.93340837E+00
77 0.86372630E+00
78 0.96330950E+00
79 0.97730780E+00
80 0.92481959E+00
81 0.10000000E+01
82 0.96358808E+00
83 0.94926485E+00
84 0.96669623E+00
85 0.89904976E+00
86 0.97412936E+00
87 0.96946553E+00
88 0.92992734E+00
89 0.96022315E+00
90 0.96988506E+00
91 0.89138371E+00
92 0.78290031E+00
93 0.88961656E+00
94 0.96366528E+00
95 0.96383074E+00
96 0.92780354E+00
97 0.90399663E+00
98 0.96240055E+00
99 0.94890056E+00
100 0.96467105E+00
101 0.86717893E+00
102 0.97515106E+00
103 0.96345678E+00
104 0.94470466E+00
105 0.96428809E+00
106 0.97527721E+00
107 0.89548278E+00
108 0.83169959E+00
109 0.10000000E+01
110 0.95622740E+00
111 0.96521701E+00
112 0.83758221E+00
mean efficiency = 0.87056536E+00
最后，本人是菜鸟，技术知识少，麻烦大神解答的过程中稍微详细一点，谢谢！！！
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