WinkelmannBoes2009: Data and Examples from Winkelmann and Boes (2009)
In AER: Applied Econometrics with R
WinkelmannBoes2009 R Documentation
Data and Examples from Winkelmann and Boes (2009)
Description
This manual page collects a list of examples from the book. Some
solutions might not be exact and the list is not complete.
If you have suggestions for improvement (preferably in the form of code),
please contact the package maintainer.
References
Winkelmann, R., and Boes, S. (2009). Analysis of Microdata, 2nd ed. Berlin and Heidelberg: Springer-Verlag.
See Also
GSS7402, GSOEP9402, PSID1976
Examples
#########################################
## US General Social Survey 1974--2002 ##
#########################################
## data
data("GSS7402", package = "AER")
## completed fertility subset
gss40 <- subset(GSS7402, age >= 40)
## Chapter 1
## Table 1.1
gss_kids <- table(gss40$kids)
cbind(absolute = gss_kids,
relative = round(prop.table(gss_kids) * 100, digits = 2))
## Table 1.2
sd1 <- function(x) sd(x) / sqrt(length(x))
with(gss40, round(cbind(
"obs" = tapply(kids, year, length),
"av kids" = tapply(kids, year, mean),
" " = tapply(kids, year, sd1),
"prop childless" = tapply(kids, year, function(x) mean(x <= 0)),
" " = tapply(kids, year, function(x) sd1(x <= 0)),
"av schooling" = tapply(education, year, mean),
" " = tapply(education, year, sd1)
), digits = 2))
## Table 1.3
gss40$trend <- gss40$year - 1974
kids_lm1 <- lm(kids ~ factor(year), data = gss40)
kids_lm2 <- lm(kids ~ trend, data = gss40)
kids_lm3 <- lm(kids ~ trend + education, data = gss40)
## Chapter 2
## Table 2.1
kids_tab <- prop.table(xtabs(~ kids + year, data = gss40), 2) * 100
round(kids_tab[,c(4, 8)], digits = 2)
## Figure 2.1
barplot(t(kids_tab[, c(4, 8)]), beside = TRUE, legend = TRUE)
## Chapter 3, Example 3.14
## Table 3.1
gss40$nokids <- factor(gss40$kids <= 0,
levels = c(FALSE, TRUE), labels = c("no", "yes"))
nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit"))
nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit"))
nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings,
data = gss40, family = binomial(link = "probit"))
## p. 87
lrtest(nokids_p1, nokids_p2, nokids_p3)
## Chapter 4, Example 4.1
gss40$nokids01 <- as.numeric(gss40$nokids) - 1
nokids_lm3 <- lm(nokids01 ~ trend + education + ethnicity + siblings, data = gss40)
coeftest(nokids_lm3, vcov = sandwich)
## Example 4.3
## Table 4.1
nokids_l1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "logit"))
nokids_l3 <- glm(nokids ~ trend + education + ethnicity + siblings,
data = gss40, family = binomial(link = "logit"))
lrtest(nokids_p3)
lrtest(nokids_l3)
## Table 4.2
nokids_xbar <- colMeans(model.matrix(nokids_l3))
sum(coef(nokids_p3) * nokids_xbar)
sum(coef(nokids_l3) * nokids_xbar)
dnorm(sum(coef(nokids_p3) * nokids_xbar))
dlogis(sum(coef(nokids_l3) * nokids_xbar))
dnorm(sum(coef(nokids_p3) * nokids_xbar)) * coef(nokids_p3)[3]
dlogis(sum(coef(nokids_l3) * nokids_xbar)) * coef(nokids_l3)[3]
exp(coef(nokids_l3)[3])
## Figure 4.4
## everything by hand (for ethnicity = "cauc" group)
nokids_xbar <- as.vector(nokids_xbar)
nokids_nd <- data.frame(education = seq(0, 20, by = 0.5), trend = nokids_xbar[2],
ethnicity = "cauc", siblings = nokids_xbar[4])
nokids_p3_fit <- predict(nokids_p3, newdata = nokids_nd,
type = "response", se.fit = TRUE)
plot(nokids_nd$education, nokids_p3_fit$fit, type = "l",
xlab = "education", ylab = "predicted probability", ylim = c(0, 0.3))
polygon(c(nokids_nd$education, rev(nokids_nd$education)),
c(nokids_p3_fit$fit + 1.96 * nokids_p3_fit$se.fit,
rev(nokids_p3_fit$fit - 1.96 * nokids_p3_fit$se.fit)),
col = "lightgray", border = "lightgray")
lines(nokids_nd$education, nokids_p3_fit$fit)
## using "effects" package (for average "ethnicity" variable)
library("effects")
nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20))
plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3))
## using "effects" plus modification by hand
nokids_p3_ef1 <- as.data.frame(nokids_p3_ef)
plot(pnorm(fit) ~ education, data = nokids_p3_ef1, type = "n", ylim = c(0, 0.3))
polygon(c(0:20, 20:0), pnorm(c(nokids_p3_ef1$upper, rev(nokids_p3_ef1$lower))),
col = "lightgray", border = "lightgray")
lines(pnorm(fit) ~ education, data = nokids_p3_ef1)
## Table 4.6
## McFadden's R^2
1 - as.numeric( logLik(nokids_p3) / logLik(nokids_p1) )
1 - as.numeric( logLik(nokids_l3) / logLik(nokids_l1) )
## McKelvey and Zavoina R^2
r2mz <- function(obj) {
ystar <- predict(obj)
sse <- sum((ystar - mean(ystar))^2)
s2 <- switch(obj$family$link, "probit" = 1, "logit" = pi^2/3, NA)
n <- length(residuals(obj))
sse / (n * s2 + sse)
}
r2mz(nokids_p3)
r2mz(nokids_l3)
## AUC
library("ROCR")
nokids_p3_pred <- prediction(fitted(nokids_p3), gss40$nokids)
nokids_l3_pred <- prediction(fitted(nokids_l3), gss40$nokids)
plot(performance(nokids_p3_pred, "tpr", "fpr"))
abline(0, 1, lty = 2)
performance(nokids_p3_pred, "auc")
plot(performance(nokids_l3_pred, "tpr", "fpr"))
abline(0, 1, lty = 2)
performance(nokids_l3_pred, "auc")@y.values
## Chapter 7
## Table 7.3
## subset selection
gss02 <- subset(GSS7402, year == 2002 & (age < 40 | !is.na(agefirstbirth)))
#Z# This selection conforms with top of page 229. However, there
#Z# are too many observations: 1374. Furthermore, there are six
#Z# observations with agefirstbirth <= 14 which will cause problems in
#Z# taking logs!
## computing time to first birth
gss02$tfb <- with(gss02, ifelse(is.na(agefirstbirth), age - 14, agefirstbirth - 14))
#Z# currently this is still needed before taking logs
gss02$tfb <- pmax(gss02$tfb, 1)
tfb_tobit <- tobit(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant,
data = gss02, left = -Inf, right = log(gss02$age - 14))
tfb_ols <- lm(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant,
data = gss02, subset = !is.na(agefirstbirth))
## Chapter 8
## Example 8.3
gss2002 <- subset(GSS7402, year == 2002 & (agefirstbirth < 40 | age < 40))
gss2002$afb <- with(gss2002, Surv(ifelse(kids > 0, agefirstbirth, age), kids > 0))
afb_km <- survfit(afb ~ 1, data = gss2002)
afb_skm <- summary(afb_km)
print(afb_skm)
with(afb_skm, plot(n.event/n.risk ~ time, type = "s"))
plot(afb_km, xlim = c(10, 40), conf.int = FALSE)
## Example 8.9
library("survival")
afb_ex <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "exponential")
afb_wb <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "weibull")
afb_ln <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "lognormal")
## Example 8.11
kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
data = gss40, family = poisson)
library("MASS")
kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
data = gss40)
lrtest(kids_pois, kids_nb)
############################################
## German Socio-Economic Panel 1994--2002 ##
############################################
## data
data("GSOEP9402", package = "AER")
## some convenience data transformations
gsoep <- GSOEP9402
gsoep$meducation2 <- cut(gsoep$meducation, breaks = c(6, 10.25, 12.25, 18),
labels = c("7-10", "10.5-12", "12.5-18"))
gsoep$year2 <- factor(gsoep$year)
## Chapter 1
## Table 1.4 plus visualizations
gsoep_tab <- xtabs(~ meducation2 + school, data = gsoep)
round(prop.table(gsoep_tab, 1) * 100, digits = 2)
spineplot(gsoep_tab)
plot(school ~ meducation, data = gsoep, breaks = c(7, 10.25, 12.25, 18))
plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18))
## Chapter 5
## Table 5.1
library("nnet")
gsoep_mnl <- multinom(
school ~ meducation + memployment + log(income) + log(size) + parity + year2,
data = gsoep)
coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),]
## alternatively
library("mlogit")
gsoep_mnl2 <- mlogit(school ~ 0 | meducation + memployment + log(income) +
log(size) + parity + year2, data = gsoep, shape = "wide", reflevel = "Hauptschule")
coeftest(gsoep_mnl2)[1:12,]
## Table 5.2
library("effects")
gsoep_eff <- effect("meducation", gsoep_mnl,
xlevels = list(meducation = sort(unique(gsoep$meducation))))
gsoep_eff$prob
plot(gsoep_eff, confint = FALSE)
## Table 5.3, odds
exp(coef(gsoep_mnl)[, "meducation"])
## all effects
eff_mnl <- allEffects(gsoep_mnl)
plot(eff_mnl, ask = FALSE, confint = FALSE)
plot(eff_mnl, ask = FALSE, style = "stacked", colors = gray.colors(3))
## omit year
gsoep_mnl1 <- multinom(
school ~ meducation + memployment + log(income) + log(size) + parity,
data = gsoep)
lrtest(gsoep_mnl, gsoep_mnl1)
eff_mnl1 <- allEffects(gsoep_mnl1)
plot(eff_mnl1, ask = FALSE, confint = FALSE)
plot(eff_mnl1, ask = FALSE, style = "stacked", colors = gray.colors(3))
## Chapter 6
## Table 6.1
library("MASS")
gsoep$munemp <- factor(gsoep$memployment != "none",
levels = c(FALSE, TRUE), labels = c("no", "yes"))
gsoep_pop <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2,
data = gsoep, method = "probit", Hess = TRUE)
gsoep_pol <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2,
data = gsoep, Hess = TRUE)
lrtest(gsoep_pop)
lrtest(gsoep_pol)
## Table 6.2
## todo
eff_pol <- allEffects(gsoep_pol)
plot(eff_pol, ask = FALSE, confint = FALSE)
plot(eff_pol, ask = FALSE, style = "stacked", colors = gray.colors(3))
####################################
## Labor Force Participation Data ##
####################################
## Mroz data
data("PSID1976", package = "AER")
PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000)
## visualizations
plot(hours ~ nwincome, data = PSID1976,
xlab = "Non-wife income (in USD 1000)",
ylab = "Hours of work in 1975")
plot(jitter(hours, 200) ~ jitter(wage, 50), data = PSID1976,
xlab = "Wife's average hourly wage (jittered)",
ylab = "Hours of work in 1975 (jittered)")
## Chapter 1, p. 18
hours_lm <- lm(hours ~ wage + nwincome + youngkids + oldkids, data = PSID1976,
subset = participation == "yes")
## Chapter 7
## Example 7.2, Table 7.1
hours_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976)
hours_ols1 <- lm(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976)
hours_ols2 <- lm(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976, subset = participation == "yes")
## Example 7.10, Table 7.4
wage_ols <- lm(log(wage) ~ education + experience + I(experience^2),
data = PSID1976, subset = participation == "yes")
library("sampleSelection")
wage_ghr <- selection(participation ~ nwincome + age + youngkids + oldkids +
education + experience + I(experience^2),
log(wage) ~ education + experience + I(experience^2), data = PSID1976)
## Exercise 7.13
hours_cragg1 <- glm(participation ~ nwincome + education +
experience + I(experience^2) + age + youngkids + oldkids,
data = PSID1976, family = binomial(link = "probit"))
library("truncreg")
hours_cragg2 <- truncreg(hours ~ nwincome + education +
experience + I(experience^2) + age + youngkids + oldkids,
data = PSID1976, subset = participation == "yes")
## Exercise 7.15
wage_olscoef <- sapply(c(-Inf, 0.5, 1, 1.5, 2), function(censpoint)
coef(lm(log(wage) ~ education + experience + I(experience^2),
data = PSID1976[log(PSID1976$wage) > censpoint,])))
wage_mlcoef <- sapply(c(0.5, 1, 1.5, 2), function(censpoint)
coef(tobit(log(wage) ~ education + experience + I(experience^2),
data = PSID1976, left = censpoint)))
##################################
## Choice of Brand for Crackers ##
##################################
## data
library("mlogit")
data("Cracker", package = "mlogit")
head(Cracker, 3)
crack <- mlogit.data(Cracker, varying = 2:13, shape = "wide", choice = "choice")
head(crack, 12)
## Table 5.6 (model 3 probably not fully converged in W&B)
crack$price <- crack$price/100
crack_mlogit1 <- mlogit(choice ~ price | 0, data = crack, reflevel = "private")
crack_mlogit2 <- mlogit(choice ~ price | 1, data = crack, reflevel = "private")
crack_mlogit3 <- mlogit(choice ~ price + feat + disp | 1, data = crack,
reflevel = "private")
lrtest(crack_mlogit1, crack_mlogit2, crack_mlogit3)
## IIA test
crack_mlogit_all <- update(crack_mlogit2, reflevel = "nabisco")
crack_mlogit_res <- update(crack_mlogit_all,
alt.subset = c("keebler", "nabisco", "sunshine"))
hmftest(crack_mlogit_all, crack_mlogit_res)
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| WinkelmannBoes2009 | R Documentation |
Data and Examples from Winkelmann and Boes (2009)
Description
This manual page collects a list of examples from the book. Some solutions might not be exact and the list is not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer.
References
Winkelmann, R., and Boes, S. (2009). Analysis of Microdata, 2nd ed. Berlin and Heidelberg: Springer-Verlag.
See Also
GSS7402, GSOEP9402, PSID1976
Examples
#########################################
## US General Social Survey 1974--2002 ##
#########################################
## data
data("GSS7402", package = "AER")
## completed fertility subset
gss40 <- subset(GSS7402, age >= 40)
## Chapter 1
## Table 1.1
gss_kids <- table(gss40$kids)
cbind(absolute = gss_kids,
relative = round(prop.table(gss_kids) * 100, digits = 2))
## Table 1.2
sd1 <- function(x) sd(x) / sqrt(length(x))
with(gss40, round(cbind(
"obs" = tapply(kids, year, length),
"av kids" = tapply(kids, year, mean),
" " = tapply(kids, year, sd1),
"prop childless" = tapply(kids, year, function(x) mean(x <= 0)),
" " = tapply(kids, year, function(x) sd1(x <= 0)),
"av schooling" = tapply(education, year, mean),
" " = tapply(education, year, sd1)
), digits = 2))
## Table 1.3
gss40$trend <- gss40$year - 1974
kids_lm1 <- lm(kids ~ factor(year), data = gss40)
kids_lm2 <- lm(kids ~ trend, data = gss40)
kids_lm3 <- lm(kids ~ trend + education, data = gss40)
## Chapter 2
## Table 2.1
kids_tab <- prop.table(xtabs(~ kids + year, data = gss40), 2) * 100
round(kids_tab[,c(4, 8)], digits = 2)
## Figure 2.1
barplot(t(kids_tab[, c(4, 8)]), beside = TRUE, legend = TRUE)
## Chapter 3, Example 3.14
## Table 3.1
gss40$nokids <- factor(gss40$kids <= 0,
levels = c(FALSE, TRUE), labels = c("no", "yes"))
nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit"))
nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit"))
nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings,
data = gss40, family = binomial(link = "probit"))
## p. 87
lrtest(nokids_p1, nokids_p2, nokids_p3)
## Chapter 4, Example 4.1
gss40$nokids01 <- as.numeric(gss40$nokids) - 1
nokids_lm3 <- lm(nokids01 ~ trend + education + ethnicity + siblings, data = gss40)
coeftest(nokids_lm3, vcov = sandwich)
## Example 4.3
## Table 4.1
nokids_l1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "logit"))
nokids_l3 <- glm(nokids ~ trend + education + ethnicity + siblings,
data = gss40, family = binomial(link = "logit"))
lrtest(nokids_p3)
lrtest(nokids_l3)
## Table 4.2
nokids_xbar <- colMeans(model.matrix(nokids_l3))
sum(coef(nokids_p3) * nokids_xbar)
sum(coef(nokids_l3) * nokids_xbar)
dnorm(sum(coef(nokids_p3) * nokids_xbar))
dlogis(sum(coef(nokids_l3) * nokids_xbar))
dnorm(sum(coef(nokids_p3) * nokids_xbar)) * coef(nokids_p3)[3]
dlogis(sum(coef(nokids_l3) * nokids_xbar)) * coef(nokids_l3)[3]
exp(coef(nokids_l3)[3])
## Figure 4.4
## everything by hand (for ethnicity = "cauc" group)
nokids_xbar <- as.vector(nokids_xbar)
nokids_nd <- data.frame(education = seq(0, 20, by = 0.5), trend = nokids_xbar[2],
ethnicity = "cauc", siblings = nokids_xbar[4])
nokids_p3_fit <- predict(nokids_p3, newdata = nokids_nd,
type = "response", se.fit = TRUE)
plot(nokids_nd$education, nokids_p3_fit$fit, type = "l",
xlab = "education", ylab = "predicted probability", ylim = c(0, 0.3))
polygon(c(nokids_nd$education, rev(nokids_nd$education)),
c(nokids_p3_fit$fit + 1.96 * nokids_p3_fit$se.fit,
rev(nokids_p3_fit$fit - 1.96 * nokids_p3_fit$se.fit)),
col = "lightgray", border = "lightgray")
lines(nokids_nd$education, nokids_p3_fit$fit)
## using "effects" package (for average "ethnicity" variable)
library("effects")
nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20))
plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3))
## using "effects" plus modification by hand
nokids_p3_ef1 <- as.data.frame(nokids_p3_ef)
plot(pnorm(fit) ~ education, data = nokids_p3_ef1, type = "n", ylim = c(0, 0.3))
polygon(c(0:20, 20:0), pnorm(c(nokids_p3_ef1$upper, rev(nokids_p3_ef1$lower))),
col = "lightgray", border = "lightgray")
lines(pnorm(fit) ~ education, data = nokids_p3_ef1)
## Table 4.6
## McFadden's R^2
1 - as.numeric( logLik(nokids_p3) / logLik(nokids_p1) )
1 - as.numeric( logLik(nokids_l3) / logLik(nokids_l1) )
## McKelvey and Zavoina R^2
r2mz <- function(obj) {
ystar <- predict(obj)
sse <- sum((ystar - mean(ystar))^2)
s2 <- switch(obj$family$link, "probit" = 1, "logit" = pi^2/3, NA)
n <- length(residuals(obj))
sse / (n * s2 + sse)
}
r2mz(nokids_p3)
r2mz(nokids_l3)
## AUC
library("ROCR")
nokids_p3_pred <- prediction(fitted(nokids_p3), gss40$nokids)
nokids_l3_pred <- prediction(fitted(nokids_l3), gss40$nokids)
plot(performance(nokids_p3_pred, "tpr", "fpr"))
abline(0, 1, lty = 2)
performance(nokids_p3_pred, "auc")
plot(performance(nokids_l3_pred, "tpr", "fpr"))
abline(0, 1, lty = 2)
performance(nokids_l3_pred, "auc")@y.values
## Chapter 7
## Table 7.3
## subset selection
gss02 <- subset(GSS7402, year == 2002 & (age < 40 | !is.na(agefirstbirth)))
#Z# This selection conforms with top of page 229. However, there
#Z# are too many observations: 1374. Furthermore, there are six
#Z# observations with agefirstbirth <= 14 which will cause problems in
#Z# taking logs!
## computing time to first birth
gss02$tfb <- with(gss02, ifelse(is.na(agefirstbirth), age - 14, agefirstbirth - 14))
#Z# currently this is still needed before taking logs
gss02$tfb <- pmax(gss02$tfb, 1)
tfb_tobit <- tobit(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant,
data = gss02, left = -Inf, right = log(gss02$age - 14))
tfb_ols <- lm(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant,
data = gss02, subset = !is.na(agefirstbirth))
## Chapter 8
## Example 8.3
gss2002 <- subset(GSS7402, year == 2002 & (agefirstbirth < 40 | age < 40))
gss2002$afb <- with(gss2002, Surv(ifelse(kids > 0, agefirstbirth, age), kids > 0))
afb_km <- survfit(afb ~ 1, data = gss2002)
afb_skm <- summary(afb_km)
print(afb_skm)
with(afb_skm, plot(n.event/n.risk ~ time, type = "s"))
plot(afb_km, xlim = c(10, 40), conf.int = FALSE)
## Example 8.9
library("survival")
afb_ex <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "exponential")
afb_wb <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "weibull")
afb_ln <- survreg(
afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16,
data = gss2002, dist = "lognormal")
## Example 8.11
kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
data = gss40, family = poisson)
library("MASS")
kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
data = gss40)
lrtest(kids_pois, kids_nb)
############################################
## German Socio-Economic Panel 1994--2002 ##
############################################
## data
data("GSOEP9402", package = "AER")
## some convenience data transformations
gsoep <- GSOEP9402
gsoep$meducation2 <- cut(gsoep$meducation, breaks = c(6, 10.25, 12.25, 18),
labels = c("7-10", "10.5-12", "12.5-18"))
gsoep$year2 <- factor(gsoep$year)
## Chapter 1
## Table 1.4 plus visualizations
gsoep_tab <- xtabs(~ meducation2 + school, data = gsoep)
round(prop.table(gsoep_tab, 1) * 100, digits = 2)
spineplot(gsoep_tab)
plot(school ~ meducation, data = gsoep, breaks = c(7, 10.25, 12.25, 18))
plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18))
## Chapter 5
## Table 5.1
library("nnet")
gsoep_mnl <- multinom(
school ~ meducation + memployment + log(income) + log(size) + parity + year2,
data = gsoep)
coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),]
## alternatively
library("mlogit")
gsoep_mnl2 <- mlogit(school ~ 0 | meducation + memployment + log(income) +
log(size) + parity + year2, data = gsoep, shape = "wide", reflevel = "Hauptschule")
coeftest(gsoep_mnl2)[1:12,]
## Table 5.2
library("effects")
gsoep_eff <- effect("meducation", gsoep_mnl,
xlevels = list(meducation = sort(unique(gsoep$meducation))))
gsoep_eff$prob
plot(gsoep_eff, confint = FALSE)
## Table 5.3, odds
exp(coef(gsoep_mnl)[, "meducation"])
## all effects
eff_mnl <- allEffects(gsoep_mnl)
plot(eff_mnl, ask = FALSE, confint = FALSE)
plot(eff_mnl, ask = FALSE, style = "stacked", colors = gray.colors(3))
## omit year
gsoep_mnl1 <- multinom(
school ~ meducation + memployment + log(income) + log(size) + parity,
data = gsoep)
lrtest(gsoep_mnl, gsoep_mnl1)
eff_mnl1 <- allEffects(gsoep_mnl1)
plot(eff_mnl1, ask = FALSE, confint = FALSE)
plot(eff_mnl1, ask = FALSE, style = "stacked", colors = gray.colors(3))
## Chapter 6
## Table 6.1
library("MASS")
gsoep$munemp <- factor(gsoep$memployment != "none",
levels = c(FALSE, TRUE), labels = c("no", "yes"))
gsoep_pop <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2,
data = gsoep, method = "probit", Hess = TRUE)
gsoep_pol <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2,
data = gsoep, Hess = TRUE)
lrtest(gsoep_pop)
lrtest(gsoep_pol)
## Table 6.2
## todo
eff_pol <- allEffects(gsoep_pol)
plot(eff_pol, ask = FALSE, confint = FALSE)
plot(eff_pol, ask = FALSE, style = "stacked", colors = gray.colors(3))
####################################
## Labor Force Participation Data ##
####################################
## Mroz data
data("PSID1976", package = "AER")
PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000)
## visualizations
plot(hours ~ nwincome, data = PSID1976,
xlab = "Non-wife income (in USD 1000)",
ylab = "Hours of work in 1975")
plot(jitter(hours, 200) ~ jitter(wage, 50), data = PSID1976,
xlab = "Wife's average hourly wage (jittered)",
ylab = "Hours of work in 1975 (jittered)")
## Chapter 1, p. 18
hours_lm <- lm(hours ~ wage + nwincome + youngkids + oldkids, data = PSID1976,
subset = participation == "yes")
## Chapter 7
## Example 7.2, Table 7.1
hours_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976)
hours_ols1 <- lm(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976)
hours_ols2 <- lm(hours ~ nwincome + education + experience + I(experience^2) +
age + youngkids + oldkids, data = PSID1976, subset = participation == "yes")
## Example 7.10, Table 7.4
wage_ols <- lm(log(wage) ~ education + experience + I(experience^2),
data = PSID1976, subset = participation == "yes")
library("sampleSelection")
wage_ghr <- selection(participation ~ nwincome + age + youngkids + oldkids +
education + experience + I(experience^2),
log(wage) ~ education + experience + I(experience^2), data = PSID1976)
## Exercise 7.13
hours_cragg1 <- glm(participation ~ nwincome + education +
experience + I(experience^2) + age + youngkids + oldkids,
data = PSID1976, family = binomial(link = "probit"))
library("truncreg")
hours_cragg2 <- truncreg(hours ~ nwincome + education +
experience + I(experience^2) + age + youngkids + oldkids,
data = PSID1976, subset = participation == "yes")
## Exercise 7.15
wage_olscoef <- sapply(c(-Inf, 0.5, 1, 1.5, 2), function(censpoint)
coef(lm(log(wage) ~ education + experience + I(experience^2),
data = PSID1976[log(PSID1976$wage) > censpoint,])))
wage_mlcoef <- sapply(c(0.5, 1, 1.5, 2), function(censpoint)
coef(tobit(log(wage) ~ education + experience + I(experience^2),
data = PSID1976, left = censpoint)))
##################################
## Choice of Brand for Crackers ##
##################################
## data
library("mlogit")
data("Cracker", package = "mlogit")
head(Cracker, 3)
crack <- mlogit.data(Cracker, varying = 2:13, shape = "wide", choice = "choice")
head(crack, 12)
## Table 5.6 (model 3 probably not fully converged in W&B)
crack$price <- crack$price/100
crack_mlogit1 <- mlogit(choice ~ price | 0, data = crack, reflevel = "private")
crack_mlogit2 <- mlogit(choice ~ price | 1, data = crack, reflevel = "private")
crack_mlogit3 <- mlogit(choice ~ price + feat + disp | 1, data = crack,
reflevel = "private")
lrtest(crack_mlogit1, crack_mlogit2, crack_mlogit3)
## IIA test
crack_mlogit_all <- update(crack_mlogit2, reflevel = "nabisco")
crack_mlogit_res <- update(crack_mlogit_all,
alt.subset = c("keebler", "nabisco", "sunshine"))
hmftest(crack_mlogit_all, crack_mlogit_res)
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