用BC模型95的话,生产函数是超越对数,只需要看the final mle estimates are后面的结果么?前面的一点都不用管么?
1、beta系数反应的就是投入的弹性是吗?t值是看绝对值还是只有正值才有效?
2、gamma值0.27说明什么呢?是不是说只有27%是由技术无效率造成的?
急求各位大侠路过指点!帮忙看看我的结果
the final mle estimates are :
coefficient standard-error t-ratio
beta 0 0.32351273E+01 0.95346939E+00 0.33930060E+01
beta 1 0.31219868E-01 0.44484156E-01 0.70181995E+00
beta 2 -0.10060408E-01 0.25265472E-02 -0.39818802E+01
beta 3 -0.45777257E+00 0.25155468E+00 -0.18197736E+01
beta 4 0.90283033E+00 0.24908855E+00 0.36245357E+01
beta 5 0.49359843E+00 0.24490932E+00 0.20154334E+01
beta 6 -0.12989856E+01 0.30533606E+00 -0.42542818E+01
beta 7 -0.33982752E+00 0.11010338E+00 -0.30864404E+01
beta 8 0.37429103E-01 0.10144309E+00 0.36896653E+00
beta 9 0.32492388E-01 0.10029405E+00 0.32397124E+00
beta10 0.31800568E-02 0.72863003E-01 0.43644328E-01
beta11 -0.57492099E-01 0.10351513E+00 -0.55539802E+00
beta12 -0.18129536E-01 0.10194944E+00 -0.17782869E+00
beta13 0.33199176E+00 0.73832469E-01 0.44965550E+01
beta14 0.29233018E-01 0.44813076E-01 0.65233232E+00
beta15 0.35853786E-01 0.54761565E-01 0.65472537E+00
beta16 0.35504242E+00 0.81562364E-01 0.43530178E+01
beta17 -0.18332139E-01 0.79304492E-02 -0.23116142E+01
beta18 0.12535562E-01 0.74822260E-02 0.16753787E+01
beta19 -0.15568619E-01 0.74303122E-02 -0.20952846E+01
beta20 0.15056174E-01 0.96832643E-02 0.15548655E+01
delta 1 0.17127400E+00 0.27218169E-01 0.62926349E+01
delta 2 -0.17978823E-01 0.33479073E-01 -0.53701673E+00
delta 3 0.70799295E-02 0.41669875E-02 0.16990522E+01
delta 4 0.37859767E+00 0.92353343E-01 0.40994474E+01
delta 5 -0.20697975E+00 0.41501285E-01 -0.49873093E+01
sigma-squared 0.11459911E+00 0.10042113E-01 0.11411852E+02
gamma 0.27915596E+00 0.13848477E+00 0.20157882E+01
log likelihood function = -0.41758725E+03
LR test of the one-sided error = 0.40662648E+02
with number of restrictions = 6
[note that this statistic has a mixed chi-square distribution]
number of iterations = 44
(maximum number of iterations set at : 100)
number of cross-sections = 140
number of time periods = 10
total number of observations = 1400
thus there are: 0 obsns not in the panel