geweihao
我用frontier4.1对某年度各地区的电子通信行业的工业增加值,固定资产净值余额以及平均从业人员数利用C-D函数求技术效率时得到下面结果,请问为什么结果都这么接近1呢,难道不同地区的技术效率几乎没有差异?显然不是啊?请问到底是哪里出了问题?我用的instruction文件附在后面,请指点,如果能帮我解决这个问题,将拨1000现金到您帐户,非常感谢.(处理过程是先求各指标对数,形成txt格式数据,修改ins文件然后做估计)可直接邮件和我联系。chaizhixian@sina.com
technical efficiency estimates :
firm eff.-est.
1 0.99924769E+00
2 0.99924727E+00
3 0.99924711E+00
4 0.99924557E+00
5 0.99924892E+00
6 0.99924685E+00
7 0.99924677E+00
8 0.99924658E+00
9 0.99924702E+00
10 0.99924666E+00
11 0.99924626E+00
12 0.99924686E+00
13 0.99924694E+00
14 0.99924551E+00
15 0.99924694E+00
16 0.99924728E+00
17 0.99924742E+00
18 0.99924644E+00
19 0.99924617E+00
20 0.99924643E+00
21 0.99924613E+00
22 0.99924663E+00
23 0.99924622E+00
24 0.99924615E+00
25 0.99924703E+00
26 0.99924606E+00
27 0.99924626E+00
28 0.99924709E+00
29 0.99924620E+00
30 0.99924641E+00
mean efficiency = 0.99924669E+00
instruction文件内容如下:
1 1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
eg8-dta.txt DATA FILE NAME
eg8-out.txt OUTPUT FILE NAME
1 1=PRODUCTION FUNCTION, 2=COST FUNCTION
y LOGGED DEPENDENT VARIABLE (Y/N)
30 NUMBER OF CROSS-SECTIONS
1 NUMBER OF TIME PERIODS
30 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.
所用数据:
1 1 5.282188 4.807294 2.13061
2 1 5.146331 5.081404 2.288486
3 1 3.068518 3.555348 .6205765
4 1 1.108563 1.99061 -.0833816
5 1 2.757475 .6312718 -.8439701
6 1 4.295651 4.252772 1.824549
7 1 2.134166 3.314186 .0487901
8 1 1.690096 2.867899 -.210721
9 1 5.67057 6.007092 2.90252
10 1 6.416994 6.136214 3.756071
11 1 4.954982 4.629863 2.754934
12 1 2.821379 2.442347 .5481214
13 1 5.241906 4.725616 2.566487
14 1 2.183802 2.261763 .8586616
15 1 5.081342 4.573679 2.427454
16 1 3.185939 3.663562 .6151857
17 1 3.839667 2.923162 1.05779
18 1 2.754934 3.906005 .7884574
19 1 7.211896 6.665047 4.73576
20 1 1.581038 1.547562 -.2357223
21 1 -1.07881 .1823216 -2.302585
22 1 1.581038 1.856298 -.356675
23 1 4.219949 4.234107 2.157559
24 1 1.965713 2.197225 .2700271
25 1 .4946962 -.2231435 -1.560648
26 1 3.465111 4.037774 1.619388
27 1 1.699279 2.163323 -.0202027
28 1 -2.995732 -4.60517 -4.60517
29 1 -1.469676 .8020016 -2.65926
30 1 -3.506558 -4.60517 -4.60517