关于frontier4.01软件！讨论者有奖励！问题时刻更新！ - 第15页

141楼

zhbs 发表于 2007-4-28 10:52:00

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

142楼

binghe78

发表于 2007-6-5 18:46:00

很不错的交流地方

吾自幼聪慧，始学文，屡考不中；遂习武，进靶场，发一箭，中鼓吏，被逐出场；偶得小恙，自制良方，服之卒。

143楼

qiuguihua2000 发表于 2007-6-7 01:35:00

你有没有好好看他的使用说明啊　　很简单的啊

我帅，故，我在。

144楼

人人 发表于 2007-6-7 22:00:00

现在本人没有时间搞这个东西了,天天在加班,对不起大家了!不过可以给大家提供一些寻找这类资料的相关地方!

武汉飞扬-本地光盘印刷专家【www.whfycd.com】武汉光盘刻录武汉光盘打印武汉光盘印刷武汉光盘封面打印武汉光盘封面印刷 QQ：327048691 MSN:felling4307@163.com 以感恩之心面对生活!

145楼

zgf0910 发表于 2007-7-5 22:05:00

好地方，不收敛怎么办？

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？

微博开通，欢迎关注http://weibo.com/1624724810

146楼

lswgdhy 发表于 2007-8-12 22:41:00

看看

未经一番寒彻骨，哪得梅花扑鼻香

147楼

robit 发表于 2007-8-12 23:11:00

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