想要分析生猪养殖不同规模的效率问题，用面板数据，每年80个样本，共6年数据进行分析。如果用CD生产函数模型进行分析结果没有问题，可是如果用技术中性的超越对数生产函数分析，估计结果如下：得到的每年的技术效率都一样,问题出在那里?请高手赐教,万分感谢.
1 1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
tr2.dta DATA FILE NAME
tr2.out OUTPUT FILE NAME
1 1=PRODUCTION FUNCTION, 2=COST FUNCTION
y LOGGED DEPENDENT VARIABLE (Y/N)
80 NUMBER OF CROSS-SECTIONS
6 NUMBER OF TIME PERIODS
480 NUMBER OF OBSERVATIONS IN TOTAL
15 NUMBER OF REGRESSOR VARIABLES (Xs)
Y MU (Y/N) [OR DELTA0 (Y/N) IF USING TE EFFECTS MODEL]
Y 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.
the final mle estimates are :
coefficient standard-error t-ratio
beta 0 -0.48362151E+01 0.10000000E+01 -0.48362151E+01
beta 1 0.52152261E-01 0.10000000E+01 0.52152261E-01
beta 2 0.15596202E+01 0.10000000E+01 0.15596202E+01
beta 3 0.12294481E+01 0.10000000E+01 0.12294481E+01
beta 4 0.11591420E+01 0.10000000E+01 0.11591420E+01
beta 5 0.95976234E-02 0.10000000E+01 0.95976234E-02
beta 6 -0.48330121E-01 0.10000000E+01 -0.48330121E-01
beta 7 -0.27089499E-02 0.10000000E+01 -0.27089499E-02
beta 8 0.43349368E-01 0.10000000E+01 0.43349368E-01
beta 9 -0.17698714E-01 0.10000000E+01 -0.17698714E-01
beta10 -0.36211612E-01 0.10000000E+01 -0.36211612E-01
beta11 -0.19410827E+00 0.10000000E+01 -0.19410827E+00
beta12 -0.11910211E+00 0.10000000E+01 -0.11910211E+00
beta13 -0.73167896E-01 0.10000000E+01 -0.73167896E-01
beta14 -0.10498672E-02 0.10000000E+01 -0.10498672E-02
beta15 0.65802145E-02 0.10000000E+01 0.65802145E-02
sigma-squared 0.78092794E-02 0.10000000E+01 0.78092794E-02
gamma 0.68000000E+00 0.10000000E+01 0.68000000E+00
mu 0.00000000E+00 0.10000000E+01 0.00000000E+00
eta 0.00000000E+00 0.10000000E+01 0.00000000E+00
log likelihood function = 0.67639930E+03
LR test of the one-sided error = 0.11354046E+03
with number of restrictions = 3
[note that this statistic has a mixed chi-square distribution]
number of iterations = 1
(maximum number of iterations set at : 100)
number of cross-sections = 80
number of time periods = 6
total number of observations = 480
thus there are: 0 obsns not in the panel
technical efficiency estimates :
efficiency estimates for year 1 :
firm eff.-est.
1 0.97170502E+00
2 0.97697659E+00
3 0.98013832E+00
4 0.99037321E+00
5 0.95611261E+00
6 0.93209434E+00
7 0.99089211E+00
8 0.93453821E+00
9 0.97818312E+00
10 0.96667362E+00
11 0.92322724E+00
12 0.95031424E+00
13 0.89458513E+00
14 0.92317893E+00
15 0.94647475E+00
16 0.93690453E+00
17 0.96672425E+00
18 0.98465733E+00
19 0.90597041E+00
20 0.94484994E+00
21 0.96149912E+00
22 0.95634897E+00
23 0.95738714E+00
24 0.99178092E+00
25 0.92957659E+00
26 0.90069890E+00
27 0.97646169E+00
28 0.96340602E+00
29 0.97390353E+00
30 0.93835277E+00
31 0.96147189E+00
32 0.99578393E+00
33 0.94785821E+00
34 0.93851759E+00
35 0.89817946E+00
36 0.90585840E+00
37 0.98789795E+00
38 0.95955731E+00
39 0.91390484E+00
40 0.85237066E+00
41 0.92615052E+00
42 0.94195278E+00
43 0.94578816E+00
44 0.98922499E+00
45 0.93497212E+00
46 0.88748118E+00
47 0.98684241E+00
48 0.93804995E+00
49 0.97623528E+00
50 0.91492013E+00
51 0.94120080E+00
52 0.98693255E+00
53 0.97221139E+00
54 0.92240951E+00
55 0.91739410E+00
56 0.87092168E+00
57 0.98881884E+00
58 0.96112513E+00
59 0.91978564E+00
60 0.91305750E+00
61 0.92346994E+00
62 0.91973672E+00
63 0.92338746E+00
64 0.98133974E+00
65 0.90212428E+00
66 0.91770219E+00
67 0.91666810E+00
68 0.87871598E+00
69 0.94101316E+00
70 0.89030900E+00
71 0.92134060E+00
72 0.93285927E+00
73 0.93936980E+00
74 0.89462833E+00
75 0.92571870E+00
76 0.93180679E+00
77 0.97253312E+00
78 0.93934838E+00
79 0.91954370E+00
80 0.99655462E+00
mean eff. in year 1 = 0.94211293E+00
efficiency estimates for year 2 :
firm eff.-est.
1 0.97170502E+00
2 0.97697659E+00
3 0.98013832E+00
4 0.99037321E+00
5 0.95611261E+00
6 0.93209434E+00
7 0.99089211E+00
8 0.93453821E+00
9 0.97818312E+00
10 0.96667362E+00
11 0.92322724E+00
12 0.95031424E+00
13 0.89458513E+00
14 0.92317893E+00
15 0.94647475E+00
16 0.93690453E+00
17 0.96672425E+00
18 0.98465733E+00
19 0.90597041E+00
20 0.94484994E+00
21 0.96149912E+00
22 0.95634897E+00
23 0.95738714E+00
24 0.99178092E+00
25 0.92957659E+00
26 0.90069890E+00
27 0.97646169E+00
28 0.96340602E+00
29 0.97390353E+00
30 0.93835277E+00
31 0.96147189E+00
32 0.99578393E+00
33 0.94785821E+00
34 0.93851759E+00
35 0.89817946E+00
36 0.90585840E+00
37 0.98789795E+00
38 0.95955731E+00
39 0.91390484E+00
40 0.85237066E+00
41 0.92615052E+00
42 0.94195278E+00
43 0.94578816E+00
44 0.98922499E+00
45 0.93497212E+00
46 0.88748118E+00
47 0.98684241E+00
48 0.93804995E+00
49 0.97623528E+00
50 0.91492013E+00
51 0.94120080E+00
52 0.98693255E+00
53 0.97221139E+00
54 0.92240951E+00
55 0.91739410E+00
56 0.87092168E+00
57 0.98881884E+00
58 0.96112513E+00
59 0.91978564E+00
60 0.91305750E+00
61 0.92346994E+00
62 0.91973672E+00
63 0.92338746E+00
64 0.98133974E+00
65 0.90212428E+00
66 0.91770219E+00
67 0.91666810E+00
68 0.87871598E+00
69 0.94101316E+00
70 0.89030900E+00
71 0.92134060E+00
72 0.93285927E+00
73 0.93936980E+00
74 0.89462833E+00
75 0.92571870E+00
76 0.93180679E+00
77 0.97253312E+00
78 0.93934838E+00
79 0.91954370E+00
80 0.99655462E+00
mean eff. in year 2 = 0.94211293E+00
之前没有接触过，是新手,所以很多问题不明白，希望高手指教,谢谢
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