[英]How to replicate standard Tobit results in R using mhurdle package
I'm exploring multiple-hurdle models using the mhurdle
package in R. To start, I trying to simply replicate the results of a standard Tobit model run using the tobit()
function from the AER
package or the censReg()
function from the censReg
package (both produce the same results).我使用的是探索多障碍模型mhurdle
包R.首先,我想简单地使用复制标准Tobit模型运行结果tobit()
从功能AER
包或censReg()
从功能censReg
包(两者都产生相同的结果)。 Using sample data from mhurdle
, these latter two packages yield the same coefficient estimates for a Tobit regression, but I cannot replicate them using mhurdle()
.使用来自mhurdle
样本数据,后两个包为 Tobit 回归产生相同的系数估计值,但我无法使用mhurdle()
复制它们。 Here is a basic example.这是一个基本的例子。 I suspect I'm overlooking something rather simple, but I can't figure it out.我怀疑我忽略了一些相当简单的事情,但我无法弄清楚。 Any idea where the misspecification lies between the two models?知道两个模型之间的错误规格在哪里吗?
library(censReg)
library(mhurdle)
### Load sample data from the mhurdle package
data(Interview)
## mhurdle() results for standard Tobit specification
summary(mhurdle(vacations ~ 0 | linc, data=Interview, dist="n", h2=T))
## censReg() results for Tobit (same as tobit() from "AER")
summary(censReg(vacations ~ linc, data=Interview))
The mhurdle
package produces the following output: mhurdle
包产生以下输出:
Call:
mhurdle(formula = vacations ~ 0 | linc, data = Interview, dist = "n",
h2 = TRUE, method = "bfgs")
Frequency of 0: 0.848
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
h2.(Intercept) -8.97938 0.54647 -16.4315 < 0.00000000000000022 ***
h2.linc 4.98411 0.61031 8.1666 0.000000000000000222 ***
sd.sd 8.68863 0.18009 48.2463 < 0.00000000000000022 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-Likelihood: -765.67 on 3 Df
R^2 :
Coefficient of determination : 0.040817
Likelihood ratio index : NA
The censReg
and the AER
packages produce the following output: censReg
和AER
包产生以下输出:
Call:
censReg(formula = vacations ~ linc, data = Interview, method = "bfgs")
Observations:
Total Left-censored Uncensored Right-censored
1000 848 152 0
Coefficients:
Estimate Std. error t value Pr(> t)
(Intercept) -6.33252 0.53559 -11.823 < 0.0000000000000002 ***
linc 3.51493 0.46583 7.545 0.0000000000000451 ***
logSigma 1.81278 0.06231 29.091 < 0.0000000000000002 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
BFGS maximization, 44 iterations
Return code 0: successful convergence
Log-likelihood: -712.5855 on 3 Df
The mhurdle function has an argument "scaled". mhurdle 函数有一个参数“缩放”。 If its meaning is TRUE (default), the dependent variable is divided by its geometric mean.如果其含义为 TRUE(默认),则因变量除以其几何平均值。 So, in this case estimated coefficients are scaled by the geometric mean of dependent variable.因此,在这种情况下,估计系数按因变量的几何平均值进行缩放。 To obtain the standard result (tobit, censreg) just change "scaled" to FALSE.要获得标准结果(tobit、censreg),只需将“缩放”更改为 FALSE。
summary(mhurdle(vacations ~ 0 | linc, data=Interview, dist="n", h2=T, scaled = FALSE))
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