[求助]！！前沿生产函数问题，求高手帮忙！！

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我对一个超越对数生产函数用frontier4.1计算后，总是显示，

the likelihood value is less than that obtained
using ols! - try again using different starting values

然后后面的 covariance matrix  全部为零。

我将变量进行过好多种变换，问题仍然存在。

请问，什么情况下会发生此种问题？应该如何解决？

望高手不吝赐教。

[此贴子已经被作者于2008-3-25 9:48:47编辑过]

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关键词：生产函数 求高手 Likelihood covariance Different 函数 高手 帮忙 生产

自己顶一个，帮帮忙啊

我再顶一个，没有人回答，什么原因？

我下面把运算过程贴出来，还望高手不吝赐教！！

这是dat文件：
1.00       1.00    8.66    4.97    5.34     5.18     5.72    26.54    25.73    28.39    27.67    30.54    29.60    24.67    28.55    26.83    32.67       30.89       30.66
  1.00       2.00    8.74    4.97    5.44     5.26     5.72    27.02    26.16    28.43    28.61    31.09    30.10    24.71    29.55    27.70    32.71       32.69       30.67
  1.00       3.00    8.80    4.98    5.51     5.30     5.73    27.44    26.37    28.53    29.22    31.61    30.37    24.76    30.41    28.07    32.86       29.04       30.63
  1.00       4.00    8.94    4.98    5.58     5.37     5.75    27.78    26.70    28.62    29.96    32.11    30.86    24.76    31.16    28.80    33.08       35.11       30.63
  1.00       5.00    9.03    4.99    5.64     5.46     5.78    28.11    27.24    28.84    30.79    32.59    31.58    24.87    31.78    29.84    33.43       32.06       30.65
  1.00       6.00    9.14    5.00    5.66     5.56     5.81    28.30    27.78    29.04    31.45    32.88    32.28    25.00    32.03    30.88    33.75       25.93       31.95
  1.00       7.00    9.17    5.01    5.68     5.64     5.83    28.46    28.23    29.22    32.03    33.16    32.89    25.08    32.30    31.77    34.04       37.08       31.97
  1.00       8.00    9.21    5.00    5.71     5.68     5.83    28.59    28.42    29.17    32.46    33.31    33.12    25.04    32.65    32.27    33.99       34.45       32.61
  1.00       9.00    9.33    5.00    5.76     5.75     5.81    28.79    28.74    29.01    33.15    33.46    33.41    24.95    33.21    33.10    33.72       33.05       33.60
  1.00       10.00    9.62    5.00    5.82     5.80     5.79    29.10    29.02    28.94    33.79    33.70    33.61    24.99    33.89    33.69    33.52       37.13       32.91
  1.00       11.00    9.81    5.01    5.89     5.88     5.78    29.50    29.48    28.95    34.66    34.03    34.00    25.10    34.68    34.63    33.39       30.61       32.77
  1.00       12.00    9.86    5.03    5.95     5.95     5.78    29.93    29.89    29.03    35.41    34.40    34.36    25.26    35.46    35.37    33.37       30.84       33.06
  1.00       13.00    9.84    5.04    6.04     5.99     5.78    30.43    30.15    29.12    36.16    34.93    34.61    25.37    36.49    35.84    33.43       34.70       33.28
  1.00       14.00    9.82    5.05    6.11     6.01     5.79    30.86    30.35    29.22    36.76    35.39    34.80    25.48    37.38    36.15    33.50       32.20       33.59
  1.00       15.00    9.77    5.05    6.19     6.02     5.80    31.30    30.42    29.28    37.30    35.90    34.91    25.53    38.37    36.27    33.60       31.96       33.99
  1.00       16.00    9.74    5.05    6.26     6.03     5.80    31.65    30.45    29.30    37.76    36.33    34.95    25.52    39.25    36.33    33.63       34.99       34.43
  1.00       17.00    9.74    5.05    6.31     6.05     5.78    31.87    30.56    29.19    38.21    36.50    35.00    25.48    39.85    36.64    33.44       33.53       34.84
  1.00       18.00    9.74    5.04    6.36     6.07     5.77    32.07    30.61    29.08    38.63    36.70    35.03    25.41    40.47    36.88    33.27       30.47       35.15
  1.00       19.00    9.73    5.03    6.40     6.09     5.74    32.19    30.61    28.88    38.99    36.79    34.98    25.27    41.00    37.08    33.00       35.68       35.44
  1.00       20.00    9.85    5.03    6.46     6.14     5.72    32.54    30.90    28.81    39.68    36.99    35.14    25.34    41.78    37.69    32.76       24.16       35.48
  1.00       21.00    9.88    5.05    6.53     6.17     5.70    32.95    31.12    28.78    40.27    37.24    35.17    25.47    42.64    38.03    32.52       24.97       35.39

这个是ins文件
1           1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
aa.txt       DATA FILE NAME
aaout.txt       OUTPUT FILE NAME
1           1=PRODUCTION FUNCTION, 2=COST FUNCTION
y           LOGGED DEPENDENT VARIABLE (Y/N)
1         NUMBER OF CROSS-SECTIONS
21         NUMBER OF TIME PERIODS
21         NUMBER OF OBSERVATIONS IN TOTAL
16           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
                      DELTAK]
                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.

这个是结果out文件：
Output from the program FRONTIER (Version 4.1c)


instruction file = i.txt    
data file =     aa.txt    


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       0.55283909E+02 0.29740690E+02 0.18588644E+01
beta 1     -0.13444607E+02 0.65357467E+01 -0.20570881E+01
beta 2     -0.53925759E+01 0.36907860E+01 -0.14610915E+01
beta 3     -0.35809798E+01 0.33936786E+01 -0.10551912E+01
beta 4       0.77438630E+01 0.48817898E+01 0.15862754E+01
beta 5     -0.51696797E+01 0.20527183E+01 -0.25184555E+01
beta 6       0.14294458E+02 0.41013514E+01 0.34853042E+01
beta 7     -0.10861241E+02 0.33362669E+01 -0.32555071E+01
beta 8     -0.67962651E+01 0.45252150E+01 -0.15018657E+01
beta 9       0.72014084E+01 0.20045514E+01 0.35925286E+01
beta10     -0.10300554E+02 0.33463769E+01 -0.30781214E+01
beta11       0.23066457E+01 0.19214074E+01 0.12004980E+01
beta12       0.24395781E+01 0.19024666E+01 0.12823237E+01
beta13       0.28961363E+01 0.23800830E+01 0.12168215E+01
beta14       0.52660486E+01 0.19869789E+01 0.26502790E+01
beta15       0.30972389E-03 0.34384841E-02 0.90075708E-01
beta16     -0.74810801E-01 0.36037058E-01 -0.20759408E+01
sigma-squared 0.90741257E-03

log likelihood function =   0.61165276E+02

the estimates after the grid search were :

beta 0       0.55286293E+02
beta 1     -0.13444607E+02
beta 2     -0.53925759E+01
beta 3     -0.35809798E+01
beta 4       0.77438630E+01
beta 5     -0.51696797E+01
beta 6       0.14294458E+02
beta 7     -0.10861241E+02
beta 8     -0.67962651E+01
beta 9       0.72014084E+01
beta10     -0.10300554E+02
beta11       0.23066457E+01
beta12       0.24395781E+01
beta13       0.28961363E+01
beta14       0.52660486E+01
beta15       0.30972389E-03
beta16     -0.74810801E-01
sigma-squared 0.17852306E-03
gamma       0.50000000E-01
mu         0.00000000E+00
  eta is restricted to be zero


iteration =   0 func evals =   20 llf = 0.61000219E+02
  0.55286293E+02-0.13444607E+02-0.53925759E+01-0.35809798E+01 0.77438630E+01
  -0.51696797E+01 0.14294458E+02-0.10861241E+02-0.67962651E+01 0.72014084E+01
  -0.10300554E+02 0.23066457E+01 0.24395781E+01 0.28961363E+01 0.52660486E+01
  0.30972389E-03-0.74810801E-01 0.17852306E-03 0.50000000E-01 0.00000000E+00
gradient step
pt better than entering pt cannot be found
iteration =   1 func evals =   28 llf = 0.61000219E+02
  0.55286293E+02-0.13444607E+02-0.53925759E+01-0.35809798E+01 0.77438630E+01
  -0.51696797E+01 0.14294458E+02-0.10861241E+02-0.67962651E+01 0.72014084E+01
  -0.10300554E+02 0.23066457E+01 0.24395781E+01 0.28961363E+01 0.52660486E+01
  0.30972389E-03-0.74810801E-01 0.17852306E-03 0.50000000E-01 0.00000000E+00


the final mle estimates are :

          coefficient   standard-error   t-ratio

beta 0       0.55286293E+02 0.10000000E+01 0.55286293E+02
beta 1     -0.13444607E+02 0.10000000E+01 -0.13444607E+02
beta 2     -0.53925759E+01 0.10000000E+01 -0.53925759E+01
beta 3     -0.35809798E+01 0.10000000E+01 -0.35809798E+01
beta 4       0.77438630E+01 0.10000000E+01 0.77438630E+01
beta 5     -0.51696797E+01 0.10000000E+01 -0.51696797E+01
beta 6       0.14294458E+02 0.10000000E+01 0.14294458E+02
beta 7     -0.10861241E+02 0.10000000E+01 -0.10861241E+02
beta 8     -0.67962651E+01 0.10000000E+01 -0.67962651E+01
beta 9       0.72014084E+01 0.10000000E+01 0.72014084E+01
beta10     -0.10300554E+02 0.10000000E+01 -0.10300554E+02
beta11       0.23066457E+01 0.10000000E+01 0.23066457E+01
beta12       0.24395781E+01 0.10000000E+01 0.24395781E+01
beta13       0.28961363E+01 0.10000000E+01 0.28961363E+01
beta14       0.52660486E+01 0.10000000E+01 0.52660486E+01
beta15       0.30972389E-03 0.10000000E+01 0.30972389E-03
beta16     -0.74810801E-01 0.10000000E+01 -0.74810801E-01
sigma-squared 0.17852306E-03 0.10000000E+01 0.17852306E-03
gamma       0.50000000E-01 0.10000000E+01 0.50000000E-01
mu         0.00000000E+00 0.10000000E+01 0.00000000E+00
  eta is restricted to be zero

log likelihood function =   0.61000219E+02

the likelihood value is less than that obtained
using ols! - try again using different starting values
number of iterations =     1

(maximum number of iterations set at :   100)

number of cross-sections =     1

number of time periods =   21

total number of observations =   21

thus there are:     0 obsns not in the panel


covariance matrix :

0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00
0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.10000000E+01



technical efficiency estimates :


  firm         eff.-est.

    1       0.99781453E+00


mean efficiency =   0.99781453E+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

    1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 21


请问：
为什么会出现
the likelihood value is less than that obtained
using ols! - try again using different starting values

及后面的 covariance matrix 全部为零的情况？

我将变量进行过好多种变换，问题仍然存在。
什么情况下会发生此种问题？
应该如何解决？

望高手不吝赐教。

covariance matrix没有全部为零啊！对角线位数有值0.10000000E+01。

gamma太小，说明没有技术非效率，不用sfa，用ols就行

tcjy 发表于 2013-11-24 17:44
gamma太小，说明没有技术非效率，不用sfa，用ols就行

你好，如何运用ols怎么操作