Gerfin, M. (1996). Parametric and Semi-Parametric Estimation of the Binary Response
Model of Labour Market Participation. Journal of Applied Econometrics,
11, 321–339.
Examples
data("SwissLabor")
### Gerfin (1996), Table I.
fm_probit <- glm(participation ~ . + I(age^2), data = SwissLabor,
family = binomial(link = "probit"))
summary(fm_probit)
### alternatively
fm_logit <- glm(participation ~ . + I(age^2), data = SwissLabor,
family = binomial)
summary(fm_logit)
Results
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> library(AER)
Loading required package: car
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
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/SwissLabor.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SwissLabor
> ### Title: Swiss Labor Market Participation Data
> ### Aliases: SwissLabor
> ### Keywords: datasets
>
> ### ** Examples
>
> data("SwissLabor")
>
> ### Gerfin (1996), Table I.
> fm_probit <- glm(participation ~ . + I(age^2), data = SwissLabor,
+ family = binomial(link = "probit"))
> summary(fm_probit)
Call:
glm(formula = participation ~ . + I(age^2), family = binomial(link = "probit"),
data = SwissLabor)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9191 -0.9695 -0.4792 1.0209 2.4803
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.74909 1.40695 2.665 0.00771 **
income -0.66694 0.13196 -5.054 4.33e-07 ***
age 2.07530 0.40544 5.119 3.08e-07 ***
education 0.01920 0.01793 1.071 0.28428
youngkids -0.71449 0.10039 -7.117 1.10e-12 ***
oldkids -0.14698 0.05089 -2.888 0.00387 **
foreignyes 0.71437 0.12133 5.888 3.92e-09 ***
I(age^2) -0.29434 0.04995 -5.893 3.79e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1203.2 on 871 degrees of freedom
Residual deviance: 1017.2 on 864 degrees of freedom
AIC: 1033.2
Number of Fisher Scoring iterations: 4
>
> ### alternatively
> fm_logit <- glm(participation ~ . + I(age^2), data = SwissLabor,
+ family = binomial)
> summary(fm_logit)
Call:
glm(formula = participation ~ . + I(age^2), family = binomial,
data = SwissLabor)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9061 -0.9627 -0.4924 1.0171 2.3915
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.19639 2.38309 2.600 0.00932 **
income -1.10409 0.22571 -4.892 1.00e-06 ***
age 3.43661 0.68789 4.996 5.86e-07 ***
education 0.03266 0.02999 1.089 0.27611
youngkids -1.18575 0.17202 -6.893 5.46e-12 ***
oldkids -0.24094 0.08446 -2.853 0.00433 **
foreignyes 1.16834 0.20384 5.732 9.94e-09 ***
I(age^2) -0.48764 0.08519 -5.724 1.04e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1203.2 on 871 degrees of freedom
Residual deviance: 1017.6 on 864 degrees of freedom
AIC: 1033.6
Number of Fisher Scoring iterations: 4
>
>
>
>
>
> dev.off()
null device
1
>