oprobit、goprobit、regoprob、xtprobit简介理论和应用

2009-12-26 12:57:24

fentone 发表于 2009-12-25 13:52 谁能解释一下这个结果吗?

regoprob.hlp中的解释：

More formally, suppose we have an ordinal dependent variable yit which takes on the values 1, 2, ..., J, where i denotes cross-sectional units and t the time dimension of the (panel) dataset.
The random effects generalized ordered probit model estimates a set of coefficients (including one for the constant) for each of the J - 1 points at which the dependent variable can be dichotomized.
The probabilities that yit will take on each of the values 1, ..., J conditional on the individual effect ui is equal to (where Φ is the cumulative distribution of the standard normal distribution)

P( yit = 1 | ui ) = Φ(-xitβ1 - ui)
P( yit = j | ui ) = Φ(-xitβj - ui) - Φ(-xitβj-1 - ui) j = 2, ..., J - 1
P( yit = J | ui ) = 1 - Φ(-xitβJ-1 - ui)

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yangguangmei

2009-12-26 13:44:29

fentone 发表于 2009-12-25 13:52
regoprob y x1-x4,i(id)

Fitting constant-only model:

Iteration 0: log likelihood = -1435.1562  (not concave)
Iteration 1: log likelihood =  -1416.286
Iteration 2: log likelihood = -1369.8608  (not concave)
Iteration 3: log likelihood = -1368.9282
Iteration 4: log likelihood = -1368.8875
Iteration 5: log likelihood = -1368.8875

Fitting full model:

Iteration 0: log likelihood = -1427.5407  (not concave)
Iteration 1: log likelihood = -1419.9814  (not concave)
Iteration 2: log likelihood =  -1363.951
Iteration 3: log likelihood =  -1361.945
Iteration 4: log likelihood =  -1361.943  (not concave)
Iteration 5: log likelihood =  -1361.943

谁能解释一下这个结果吗?

这个结果怎么去看?或者说是什么意思