[资料] 关于frontier4.01软件！讨论者有奖励！问题时刻更新！ [推广有奖]

161楼

wangkaiye 发表于 2009-10-18 15:06:38

1             1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
eg1-dta.txt       DATA FILE NAME
eg1-out.txt       OUTPUT FILE NAME
1             1=PRODUCTION FUNCTION, 2=COST FUNCTION ------//1为生产函数，2为成本函数
y             LOGGED DEPENDENT VARIABLE (Y/N)
60             NUMBER OF CROSS-SECTIONS  //----其实就是DMU的个数
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.
-----------------------------------------------------------------------------------------------------------------------------------------------------Output from the program FRONTIER (Version 4.1d)

instruction file = eg1-ins.txt
data file =       eg1-dta.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       0.24489834E+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 =    20  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 =    42  llf = -0.17027230E+02
   0.56160697E+00 0.28108701E+00 0.53647803E+00 0.21694170E+00 0.79718731E+00
iteration =    7  func evals =    66  llf = -0.17027229E+02
   0.56161963E+00 0.28110205E+00 0.53647981E+00 0.21700046E+00 0.79720730E+00

the final mle estimates are :（最终的极大似然估计值）

               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 -0.91550466E-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 -0.91550466E-04 -0.16404645E-03  0.67450773E-02  0.18611506E-01

technical efficiency estimates :(技术效率估计值)

   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