求对Ologit回归结果的解释

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

各位大虾，我用Ologit 回归对一个模型进行估计，结果如下：
Ordered logistic regression


Number of obs
=

9085


LR chi2 (4)
=
752.61

Prob > chi2

=
0.0000
Log likelihood = -9679.7864

Pseudo R2
=
0.0374
----------------------------------------------------------------------------------------------------------------
      
|
Coef.
Std. Err.
z

P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------------------------------------------------------
    |
.0659089
.0085408
7.72

0.000
.0491691
.0826486


|
-.2536948
.0302239

-8.39

0.000
-.3129325
-.194457


  |
-.431092
.0228156
-18.89

0.000
-.4758098
-.3863742




|
-.1430947
.0315265
-4.54
0.000
-.2048855
-.081304
-------------+-------------------------------------------------------------------------------------------------

/cut1 |
-6.733663
.1747877
-7.07624
-6.391085

/cut2 |
-4.545144
.1416532
-4.822779
-4.267509

/cut3 |
-1.760224
.1333617
-2.021608
-1.49884

/cut4 |
1.062964
.1357218
.7969541
1.328974
-----------------------------------------------------------------------------------------------------------------
（结果中有一个因变量，四个自变量，但不知什么原因没有显示出了）请问各个参数是什么意思呢？谢谢

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏1 回帖

关键词：ologit logit 回归结果 Log regression 模型

路过，路过。帮你围观下，我也算是学习了。

看了下你结果数据，是显示一个因变量和四个自变量，至于为什么没显示变量名，会不会因为你命名或复制时错了，把变量名都变为“|”
参数的意思，我也不是很确定，刚查了下，找到如下一些文字，好像能说明参数的意思，但我不确定是否对错哦！我将其复制在下面，出处我也附上。若找到文献原件，希望分享哦！谢谢啦！
Consider a parameterization in which a constant is present, e.g., Greene’s formulation (Greene 2003, 736):

    Pr(Y = 0) = F(−Xb)
    Pr(Y = 1) = F(u1 −Xb) − F(−Xb)
    Pr(Y = 2) = F(u2 −Xb) − F(u1 −Xb)
    ...

In the preceding, F is the cumulative distribution function (CDF), either the cumulative standard normal distribution for ordered probit regression or the cumulative logistic distribution for ordered logistic regression. Since Greene includes a constant in his Xb, we need to indicate this to make his notation and Stata’s ordered probit/logistic notation comparable:

    Pr(Y = 0) = F(−Xb − con)
    Pr(Y = 1) = F(u1 − Xb − con) − F(−Xb − con)
    Pr(Y = 1) = F(u2 − Xb − con) − F(u1 −Xb − con)
    ...

Now, compare this with Stata’s no-constant model:

    Pr(Y = 0) = F(/cut1 − Xb)
    Pr(Y = 1) = F(/cut2 − Xb) − F(/cut1 − Xb)
    Pr(Y = 2) = F(/cut3 − Xb) − F(/cut2 − Xb)
    ...

Examining the expressions for Pr(Y = 0), we see that

    −Xb − con = /cut1 − Xb

so Greene’s constant equals –/cut1. Greene set the first cut point to zero, whereas Stata set the constant to zero.

Combining this observation with the expressions for Pr(Y = 1), we see that Greene’s u1 = /cut2 + con = /cut2 − /cut1. Doing the same for Pr(Y = 2), we see that u2 = /cut3 − /cut1. Thus to estimate Greene’s model using the coefficient estimates from Stata’s ordered probit/logistic regression commands we can use the following:

    Greene's intercept = −/cut1
    Greene's u1 = /cut2 − /cut1
    Greene's u2 = /cut3 − /cut1
    ...

After you fit your model using Stata, you can convert to Greene’s parameterization using lincom, which will provide both the coefficient estimate and the standard error as follows:

    ologit/oprobit ...
    lincom _b[/cut2] - _b[/cut1]
    lincom _b[/cut3] - _b[/cut1]
    ...

Greene, W. H. 2003. Econometric Analysis. 5th ed. Upper Saddle River, NJ: Prentice Hall.

你需要把你的变量名都列出来，看了才好给你解释 可以截个图来看看

有序回归模型！

刚遇到这一问题，看过这一课件就明白了，希望能够帮到你，谢谢！课件链接：http://wenku.baidu.com/link?url=BcdcHns29Q1Eq3ARs_EVnpfpaWbCsQnfn4zvKIsB-fD2jgg99-ufr2yeU267QIn2R81llrVowCifAQm6HN6DT2A3W7VmiPCstqzNkfB3PMO

duan2558711 发表于 2017-1-9 10:27
刚遇到这一问题，看过这一课件就明白了，希望能够帮到你，谢谢！课件链接：http://wenku.baidu.com/link?ur ...

您好，可以发一下课件吗？链接找不到，谢谢啦