求帮忙看一下在stata中box-cox转换结果-经管之家官网！

经济学管理学金融学统计学

您当前的位置> 考研考博>>

求帮忙看一下在stata中box-cox转换结果

发布：16710104 | 分类：考研

关于本站

人大经济论坛-经管之家：分享大学、考研、论文、会计、留学、数据、经济学、金融学、管理学、统计学、博弈论、统计年鉴、行业分析包括等相关资源。
经管之家是国内活跃的在线教育咨询平台!

提供"微信号、微博、抖音、快手、头条、小红书、百家号、企鹅号、UC号、一点资讯"等虚拟账号交易，真正实现买卖双方的共赢。【请点击这里访问】

TOP热门关键词

专题页面精选

免费学术公开课,扫码加入

boxcoxprices1 s2 dj dl wd1 wd2 bz yj yj ls lable wl tc yf1 yf2 sh1 sh2pj dz,lrtest
note: yj dropped because of collinearity
Fitting comparison model
Iteration 0: log likelihood = -1282.9176
Iteration 1: log likelihood =-1193.974
Iteration 2: log likelihood = -1193.9265
Iteration 3: log likelihood = -1193.9265
Fitting full model
Iteration 0: log likelihood = -1250.9672
Iteration 1: log likelihood = -1139.5853
Iteration 2: log likelihood = -1138.8727
Iteration 3: log likelihood = -1138.8724
Iteration 4: log likelihood = -1138.8724
Fitting comparison models for LR tests
Iteration 0: log likelihood = -1251.0143
Iteration 1: log likelihood = -1139.7452
Iteration 2: log likelihood = -1139.0025
Iteration 3: log likelihood = -1139.0022
Iteration 4: log likelihood = -1139.0022
Iteration 0: log likelihood = -1251.0193
Iteration 1: log likelihood = -1139.6247
Iteration 2: log likelihood =-1138.895
Iteration 3: log likelihood = -1138.8947
Iteration 4: log likelihood = -1138.8947
Iteration 0: log likelihood = -1251.0205
Iteration 1: log likelihood =-1139.662
Iteration 2: log likelihood = -1138.9803
Iteration 3: log likelihood = -1138.98
Iteration 4: log likelihood = -1138.98
Iteration 0: log likelihood = -1260.6813
Iteration 1: log likelihood = -1161.3836
Iteration 2: log likelihood =-1160.991
Iteration 3: log likelihood = -1160.9909
Iteration 4: log likelihood = -1160.9909
Iteration 0: log likelihood = -1251.3219
Iteration 1: log likelihood = -1140.1492
Iteration 2: log likelihood = -1139.4457
Iteration 3: log likelihood = -1139.4454
Iteration 4: log likelihood = -1139.4454
Iteration 0: log likelihood = -1251.1722
Iteration 1: log likelihood = -1140.1672
Iteration 2: log likelihood = -1139.4447
Iteration 3: log likelihood = -1139.4443
Iteration 4: log likelihood = -1139.4443
Iteration 0: log likelihood = -1253.9474
Iteration 1: log likelihood = -1144.8984
Iteration 2: log likelihood = -1144.1239
Iteration 3: log likelihood = -1144.1235
Iteration 4: log likelihood = -1144.1235
Iteration 0: log likelihood = -1251.0128
Iteration 1: log likelihood = -1139.7619
Iteration 2: log likelihood = -1139.0596
Iteration 3: log likelihood = -1139.0593
Iteration 4: log likelihood = -1139.0593
Iteration 0: log likelihood = -1251.0126
Iteration 1: log likelihood =-1139.589
Iteration 2: log likelihood = -1138.8994
Iteration 3: log likelihood = -1138.8991
Iteration 4: log likelihood = -1138.8991
Iteration 0: log likelihood = -1254.4156
Iteration 1: log likelihood = -1143.1787
Iteration 2: log likelihood = -1142.3763
Iteration 3: log likelihood = -1142.3759
Iteration 4: log likelihood = -1142.3759
Number of obs = 305
LR chi2(18) = 110.11
Log likelihood = -1138.8724 Prob > chi2 = 0.000
------------------------------------------------------------------------------
price | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/theta |-.2239895 .0896193 -2.50 0.012 -.39964 -.0483389
------------------------------------------------------------------------------
Estimates of scale-variant parameters
-------------------------------------------------------------
| Coef.chi2(df)P>chi2(df) df of chi2
-------------+-----------------------------------------------
Notrans |
s1 |-.0235302 0.260 0.610 1
s2 |-.0051872 0.045 0.833 1
dj | .0143059 0.215 0.643 1
dl | .1938586 44.237 0.000 1
wd1 | .0563133 1.146 0.284 1
wd2 | .0585799 1.144 0.285 1
bz | .0935278 10.502 0.001 1
yj |-.0090733 0.078 0.780 1
ls |-.0283363 1.028 0.311 1
lable | -.048456 2.111 0.146 1
wl |-.0183611 0.403 0.526 1
tc | .0466054 3.493 0.062 1
yf1 |-.1577191 6.161 0.013 1
yf2 |-.1266088 4.195 0.041 1
sh1 | .0644183 1.246 0.264 1
sh2 | .0273976 0.374 0.541 1
pj | .0033248 0.053 0.817 1
dz |-.0117198 7.007 0.008 1
_cons | 2.267277
-------------+-----------------------------------------------
/sigma | .1862849
-------------------------------------------------------------
---------------------------------------------------------
Test Restricted LR statistic P-value
H0: log likelihood chi2 Prob > chi2
---------------------------------------------------------
theta = -1 -1173.1427 68.54 0.000
theta =0 -1142.0913 6.44 0.011
theta =1 -1250.9672 224.19 0.000
---------------------------------------------------------
想了解一下这样做对不对，二分虚拟变量可否放进去转换？还有以下几个问题：
1.我的lamdb等于-0.22是否意味着我可以采用对数形还是非对数形式？解释变量是否需要进行boxcox转换？如何转换，程序如何写？
2.在下一部回归是不是这样写:gen lnp=log（price）
reg lnp s1s2 ......即可？

「经管之家」APP：经管人学习、答疑、交友，就上经管之家！
免流量费下载资料----在经管之家app可以下载论坛上的所有资源，并且不额外收取下载高峰期的论坛币。
涵盖所有经管领域的优秀内容----覆盖经济、管理、金融投资、计量统计、数据分析、国贸、财会等专业的学习宝库，各类资料应有尽有。
来自五湖四海的经管达人----已经有上千万的经管人来到这里，你可以找到任何学科方向、有共同话题的朋友。
经管之家（原人大经济论坛），跨越高校的围墙，带你走进经管知识的新世界。
扫描下方二维码下载并注册APP

本文关键词：

本文论坛网址：https://bbs.pinggu.org/thread-3839105-1-1.html

上一篇 | 你想要过得简单一点就必须从心底变得简单 ...

下一篇 | spss做因子分析，10个样本，9个指标是不是 ...

考研精彩帖子推荐更多

您可能感兴趣的文章

本站推荐的文章

人气文章

本文标题：求帮忙看一下在stata中box-cox转换结果

本文链接网址：https://bbs.pinggu.org/jg/kaoyankaobo_kaoyan_3839105_1.html

1.凡人大经济论坛-经管之家转载的文章,均出自其它媒体或其他官网介绍,目的在于传递更多的信息,并不代表本站赞同其观点和其真实性负责；
2.转载的文章仅代表原创作者观点,与本站无关。其原创性以及文中陈述文字和内容未经本站证实,本站对该文以及其中全部或者部分内容、文字的真实性、完整性、及时性，不作出任何保证或承若；
3.如本站转载稿涉及版权等问题,请作者及时联系本站,我们会及时处理。

求帮忙看一下在stata中box-cox转换结果-经管之家官网！

考研