wooldrige_tobit

handbyhand 手把手教你做出APE scale

這是一個簡單的複製 複製出wooldridge大神的mroz的例子

這一份筆記最大的用途在於

教你如何手把手的複製出 APE scale, 這樣才能讓你用APE scale乘以報表的coef 這樣才能讓你和OLS做比較

require(AER)

## Loading required package: AER

## Loading required package: car

## Loading required package: carData

## Loading required package: lmtest

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: sandwich

## Loading required package: survival

require(wooldridge)

## Loading required package: wooldridge

require(npsf)

## Loading required package: npsf

## Loading required package: Formula

## Loading required package: randtoolbox

## Loading required package: rngWELL

## This is randtoolbox. For an overview, type 'help("randtoolbox")'.

## Loading required package: sfsmisc

data(mroz)
names(mroz)

fm.tobit <- tobit(hours~nwifeinc+educ+exper+I(exper^2)+age+kidslt6+kidsge6,
data = mroz)
summary(fm.tobit)

## 
## Call:
## tobit(formula = hours ~ nwifeinc + educ + exper + I(exper^2) + 
##     age + kidslt6 + kidsge6, data = mroz)
## 
## Observations:
##          Total  Left-censored     Uncensored Right-censored 
##            753            325            428              0 
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  965.30528  446.43614   2.162 0.030599 *  
## nwifeinc      -8.81424    4.45910  -1.977 0.048077 *  
## educ          80.64561   21.58324   3.736 0.000187 ***
## exper        131.56430   17.27939   7.614 2.66e-14 ***
## I(exper^2)    -1.86416    0.53766  -3.467 0.000526 ***
## age          -54.40501    7.41850  -7.334 2.24e-13 ***
## kidslt6     -894.02174  111.87804  -7.991 1.34e-15 ***
## kidsge6      -16.21800   38.64139  -0.420 0.674701    
## Log(scale)     7.02289    0.03706 189.514  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Scale: 1122 
## 
## Gaussian distribution
## Number of Newton-Raphson Iterations: 4 
## Log-likelihood: -3819 on 9 Df
## Wald-statistic: 253.9 on 7 Df, p-value: < 2.22e-16

fm.tobit$call

## tobit(formula = hours ~ nwifeinc + educ + exper + I(exper^2) + 
##     age + kidslt6 + kidsge6, data = mroz)

#predict(fm.tobit,mroz)

x=data.frame(rep(1,753),mroz$nwifeinc,mroz$educ ,mroz$exper,mroz$exper^2,mroz$age,mroz$kidslt6,mroz$kidsge6   )

x <- as.matrix(x) #要轉成矩陣才能運算

###753這裡是樣本數
###1122 是scale 也就是sigma hat

mean(
pnorm(x%*%
fm.tobit$coefficients/1122, 0, 1)
)

## [1] 0.5886645

#請參考w大神的教科書page 602第四行

0.5886645和wooldrige教科書的一樣