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CFT_2021_JASA_empapp.R
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# Replication file: empirical illustration
# Cattaneo, Feng and Titiunik (2021)
# Notes: replication needs to load supporting functions first
# Date: Aug 7, 2021
rm(list=ls())
library(ggplot2)
library(optiSolve)
library(Qtools)
library(latex2exp)
library(Hmisc)
#setwd("~/Dropbox/SC/Empirical_Illustration/")
source("CFT_2021_JASA_empapp-fun.R")
set.seed(1234)
##############################
###### Germany ###############
data <- read.csv("Germany.csv")
### 1st differenced data ####
t <- data$year[data$index==1][-1]
y <- data$gdp[data$index==7]
x <- matrix(data$gdp[data$index!=7], 44)
y <- diff(log(data$gdp[data$index==7]))*100
x <- apply(log(x), 2, diff)*100
A <- y[t<=1990]
B <- x[t<=1990,]
C <- NULL
L <- 7
x.T <- x[(t>=1990+1&t<=1990+L),]
eq <- 1
lb <- 0
method.u <- "all"
alpha <- 0.1
M <- 100
result <- sc.pi(A=A, B=B, C=C, x.T=x.T, eq=eq, lb=lb, method.u=method.u, alpha=alpha, M=M, model=1, u.order=1, constant=F)
sc <- c(result$fit.pr, result$fit.po)
na <- rep(NA, nrow(B))
sc.l.0 <- c(na, result$sc.l.0); sc.r.0 <- c(na, result$sc.r.0)
sc.l.1 <- c(na, result$sc.l.1); sc.r.1 <- c(na, result$sc.r.1)
sc.l.2 <- c(na, result$sc.l.2); sc.r.2 <- c(na, result$sc.r.2)
sc.l.3 <- c(na, result$sc.l.3); sc.r.3 <- c(na, result$sc.r.3)
# Plot
dat <- data.frame(t=t[t<=1990+L], y=y[t<=1990+L], sname="Germany")
dat.sc <- data.frame(t=t[t<=1990+L], sc=sc,
lb0=sc.l.0, ub0=sc.r.0,
lb1=sc.l.1, ub1=sc.r.1,
lb2=sc.l.2, ub2=sc.r.2,
lb3=sc.l.3, ub3=sc.r.3,
sname="SC unit")
pdf.width <- 6
pdf.height <- 4.5
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
labs(x="year", y="growth in GDP per capita (%)") +
theme(legend.position = c(0.15,0.15),
legend.background = element_rect(fill = "transparent"))
plot <- plot + geom_line(data=dat, aes(x=t, y=y, colour=sname), linetype=1) +
geom_point(data=dat, aes(x=t, y=y, colour=sname), shape=1) +
geom_line(data=dat.sc, aes(x=t, y=sc, colour=sname), linetype=5) +
geom_point(data=dat.sc, aes(x=t, y=sc, colour=sname), col="blue", shape=19) +
geom_vline(xintercept = 1990, linetype="dotted") +
geom_text(aes(x=1990, label="\nreunification", y=10), angle=90, size=4) +
scale_x_continuous(breaks=c(seq(1960, 1990, 10), 1997))
plot0 <- plot + scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/Germany_SC.pdf", width=pdf.width, height=pdf.height)
plot.w <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/Germany_SCwithPI_w.pdf", width=pdf.width, height=pdf.height)
plot1 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb1, ymax=ub1),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/Germany_SCwithPI_1.pdf", width=pdf.width, height=pdf.height)
plot2 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb2, ymax=ub2),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/Germany_SCwithPI_2.pdf", width=pdf.width, height=pdf.height)
plot3 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb3, ymax=ub3),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/Germany_SCwithPI_3.pdf", width=pdf.width, height=pdf.height)
#### Sensitivity analysis #######
# Gaussian bound:
multi <- c(0.25, 0.5, 1, 1.5, 2)
time <- c(1993)
for (l in 1:length(time)) {
ssc.l.1 <- ssc.r.1 <- c()
u.mean <- result$u.1[time[l]-1990]
sig <- sqrt(result$u.2[time[l]-1990])
sig.seq <- multi*sig
for (s in 1:length(sig.seq)) {
eps <- sqrt(-log(alpha/4)*2*(sig.seq[s]^2))
ssc.l.1[s] <- sc.l.0[t==time[l]] + u.mean - eps
ssc.r.1[s] <- sc.r.0[t==time[l]] + u.mean + eps
}
sen.dat <- data.frame(t=c(1:5), lb1=ssc.l.1, ub1=ssc.r.1,
lb=rep(sc.l.0[t==time[l]], 5),
ub=rep(sc.r.0[t==time[l]], 5),
lab=as.factor(multi))
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="sd. of e", y="growth in GDP per capita (%)")
plot <- plot + geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb1, ymax=ub1),
col="maroon", width=0.2, linetype=5) +
geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb, ymax=ub),
col="blue", width=0.2, linetype=1) +
geom_hline(yintercept = y[t==time[l]], linetype=1, size=0.3, alpha=0.8) +
annotate("text", x=5.4, y=y[t==time[l]]-.2,label="Y(1)", size=3.5) +
scale_x_discrete(labels=c(parse(text=TeX("$0.25\\hat{\\sigma}$")),
parse(text=TeX("$0.5\\hat{\\sigma}$")),
parse(text=TeX("$\\hat{\\sigma}$")),
parse(text=TeX("$1.5\\hat{\\sigma}$")),
parse(text=TeX("$2\\hat{\\sigma}$"))))
ggsave(paste("figures/Germany_sens_", l, ".pdf", sep=""), width=pdf.width, height=pdf.height)
}
#########################
### Level ###############
data <- read.csv("Germany.csv")
t <- data$year[data$index==1]
y <- data$gdp[data$index==7]/1000
x <- matrix(data$gdp[data$index!=7], 44)/1000
A <- y[t<=1990]
B <- x[t<=1990,]
C <- NULL
L <- 7
x.T <- x[(t>=1990+1&t<=1990+L),]
eq <- 1
lb <- 0
method.u <- "all"
alpha <- 0.1
M <- 100
result <- sc.pi(A=A, B=B, C=C, x.T=x.T, eq=eq, lb=lb, method.u=method.u, alpha=alpha, M=M, model=3, u.order=1, constant=F)
sc <- c(result$fit.pr, result$fit.po)
na <- rep(NA, nrow(B))
sc.l.0 <- c(na, result$sc.l.0); sc.r.0 <- c(na, result$sc.r.0)
sc.l.1 <- c(na, result$sc.l.1); sc.r.1 <- c(na, result$sc.r.1)
sc.l.2 <- c(na, result$sc.l.2); sc.r.2 <- c(na, result$sc.r.2)
sc.l.3 <- c(na, result$sc.l.3); sc.r.3 <- c(na, result$sc.r.3)
# Plot
dat <- data.frame(t=t[t<=1990+L], y=y[t<=1990+L], sname="Germany")
dat.sc <- data.frame(t=t[t<=1990+L], sc=sc,
lb0=sc.l.0, ub0=sc.r.0,
lb1=sc.l.1, ub1=sc.r.1,
lb2=sc.l.2, ub2=sc.r.2,
lb3=sc.l.3, ub3=sc.r.3,
sname="SC unit")
pdf.width <- 6
pdf.height <- 4.5
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
labs(x="year", y="GDP per capita (thousand US dollars)") +
theme(legend.position = c(0.1, 0.95),
legend.background = element_rect(fill = "transparent"))
plot <- plot + geom_line(data=dat, aes(x=t, y=y, colour=sname), linetype=1) +
geom_point(data=dat, aes(x=t, y=y, colour=sname), shape=1) +
geom_line(data=dat.sc, aes(x=t, y=sc, colour=sname), linetype=5) +
geom_point(data=dat.sc, aes(x=t, y=sc, colour=sname), col="blue", shape=19) +
geom_vline(xintercept = 1990, linetype="dotted") +
geom_text(aes(x=1990, label="\nreunification", y=10), angle=90, size=4) +
scale_x_continuous(breaks=c(seq(1960, 1990, 10), 1997))
plot0 <- plot + scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelGermany_SC.pdf", width=pdf.width, height=pdf.height)
plot.w <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelGermany_SCwithPI_w.pdf", width=pdf.width, height=pdf.height)
plot1 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb1, ymax=ub1),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelGermany_SCwithPI_1.pdf", width=pdf.width, height=pdf.height)
plot2 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb2, ymax=ub2),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelGermany_SCwithPI_2.pdf", width=pdf.width, height=pdf.height)
plot3 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb3, ymax=ub3),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelGermany_SCwithPI_3.pdf", width=pdf.width, height=pdf.height)
#### Sensitivity analysis #######
# Gaussian bound:
multi <- c(0.25, 0.5, 1, 1.5, 2)
time <- c(1993)
for (l in 1:length(time)) {
ssc.l.1 <- ssc.r.1 <- c()
u.mean <- result$u.1[time[l]-1990]
sig <- sqrt(result$u.2[time[l]-1990])
sig.seq <- multi*sig
for (s in 1:length(sig.seq)) {
eps <- sqrt(-log(alpha/4)*2*(sig.seq[s]^2))
ssc.l.1[s] <- sc.l.0[t==time[l]] + u.mean - eps
ssc.r.1[s] <- sc.r.0[t==time[l]] + u.mean + eps
}
sen.dat <- data.frame(t=c(1:5), lb1=ssc.l.1, ub1=ssc.r.1,
lb=rep(sc.l.0[t==time[l]], 5),
ub=rep(sc.r.0[t==time[l]], 5),
lab=as.factor(multi))
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="sd. of e", y="GDP per capita (thousand US dollars)")
plot <- plot + geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb1, ymax=ub1),
col="maroon", width=0.2, linetype=5) +
geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb, ymax=ub),
col="blue", width=0.2, linetype=1) +
geom_hline(yintercept = y[t==time[l]], linetype=1, size=0.3, alpha=0.8) +
annotate("text", x=5.4, y=y[t==time[l]]-.05,label="Y(1)", size=3.5) +
scale_x_discrete(labels=c(parse(text=TeX("$0.25\\hat{\\sigma}$")),
parse(text=TeX("$0.5\\hat{\\sigma}$")),
parse(text=TeX("$\\hat{\\sigma}$")),
parse(text=TeX("$1.5\\hat{\\sigma}$")),
parse(text=TeX("$2\\hat{\\sigma}$"))))
ggsave(paste("figures/LevelGermany_sens_", l, ".pdf", sep=""), width=pdf.width, height=pdf.height)
}
############################################################################
######### California Tobacco Control #######################################
############################################################################
data <- read.csv("California.csv")
t <- data$year[data$state=="California"][-1]
y <- data$cigsale[data$state=="California"]
x <- data[data$state!="California",1:3]
x <- as.matrix(reshape(x, timevar = "state", idvar="year", direction = "wide"))[,-1]
y <- diff(log(y))*100
x <- apply(log(x), 2, diff)*100
# cutoff: 1988
A <- y[t<=1988]
B <- x[t<=1988,]
C <- NULL
L <- 7
x.T <- x[(t>=1988+1&t<=1988+L),]
eq <- 1
lb <- 0
method.u <- "all"
alpha <- 0.1
M <- 100
result <- sc.pi(A=A, B=B, C=C, x.T=x.T, eq=eq, lb=lb, method.u=method.u, alpha=alpha, M=M, model=1, u.order=1, constant=F)
sc <- c(result$fit.pr, result$fit.po)
na <- rep(NA, nrow(B))
sc.l.0 <- c(na, result$sc.l.0); sc.r.0 <- c(na, result$sc.r.0)
sc.l.1 <- c(na, result$sc.l.1); sc.r.1 <- c(na, result$sc.r.1)
sc.l.2 <- c(na, result$sc.l.2); sc.r.2 <- c(na, result$sc.r.2)
sc.l.3 <- c(na, result$sc.l.3); sc.r.3 <- c(na, result$sc.r.3)
# Plot
dat <- data.frame(t=t[t<=1988+L], y=y[t<=1988+L], sname="California")
dat.sc <- data.frame(t=t[t<=1988+L], sc=sc,
lb0=sc.l.0, ub0=sc.r.0,
lb1=sc.l.1, ub1=sc.r.1,
lb2=sc.l.2, ub2=sc.r.2,
lb3=sc.l.3, ub3=sc.r.3,
sname="SC unit")
pdf.width <- 6
pdf.height <- 4.5
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
labs(x="year", y="growth in cigarette sales per capita (%)") +
theme(legend.position = c(0.15,0.15),
legend.background = element_rect(fill = "transparent"))
plot <- plot + geom_line(data=dat, aes(x=t, y=y, colour=sname), linetype=1) +
geom_point(data=dat, aes(x=t, y=y, colour=sname), shape=1) +
geom_line(data=dat.sc, aes(x=t, y=sc, colour=sname), linetype=5) +
geom_point(data=dat.sc, aes(x=t, y=sc, colour=sname), col="blue", shape=19) +
geom_vline(xintercept=1988, linetype="dotted") +
geom_text(aes(x=1988, label="\nProposition 99", y=1.5), angle=90, size=3.5)
plot0 <- plot + scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/CA-Tobacco_SC.pdf", width=pdf.width, height=pdf.height)
plot.w <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/CA-Tobacco_SCwithPI_w.pdf", width=pdf.width, height=pdf.height)
plot1 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb1, ymax=ub1),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/CA-Tobacco_SCwithPI_1.pdf", width=pdf.width, height=pdf.height)
plot2 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb2, ymax=ub2),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/CA-Tobacco_SCwithPI_2.pdf", width=pdf.width, height=pdf.height)
plot3 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb3, ymax=ub3),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/CA-Tobacco_SCwithPI_3.pdf", width=pdf.width, height=pdf.height)
#### Sensitivity analysis #######
# Gaussian bound:
multi <- c(0.25, 0.5, 1, 1.5, 2)
time <- c(1989)
for (l in 1:length(time)) {
ssc.l.1 <- ssc.r.1 <- c()
u.mean <- result$u.1[time[l]-1988]
sig <- sqrt(result$u.2[time[l]-1988])
sig.seq <- multi*sig
for (s in 1:length(sig.seq)) {
eps <- sqrt(-log(alpha/4)*2*(sig.seq[s]^2))
ssc.l.1[s] <- sc.l.0[t==time[l]] + u.mean - eps
ssc.r.1[s] <- sc.r.0[t==time[l]] + u.mean + eps
}
sen.dat <- data.frame(t=c(1:5), lb1=ssc.l.1, ub1=ssc.r.1,
lb=rep(sc.l.0[t==time[l]], 5),
ub=rep(sc.r.0[t==time[l]], 5),
lab=as.factor(multi))
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="sd. of e", y="growth in cigarette sales per capita (%)")
plot <- plot + geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb1, ymax=ub1),
col="maroon", width=0.2, linetype=5) +
geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb, ymax=ub),
col="blue", width=0.2, linetype=1) +
geom_hline(yintercept = y[t==time[l]], linetype=1, size=0.3, alpha=0.8) +
annotate("text", x=5.4, y=y[t==time[l]]-.4,label="Y(1)", size=3.5) +
scale_x_discrete(labels=c(parse(text=TeX("$0.25\\hat{\\sigma}$")),
parse(text=TeX("$0.5\\hat{\\sigma}$")),
parse(text=TeX("$\\hat{\\sigma}$")),
parse(text=TeX("$1.5\\hat{\\sigma}$")),
parse(text=TeX("$2\\hat{\\sigma}$"))))
ggsave(paste("figures/CA_sens_", l, ".pdf", sep=""), width=pdf.width, height=pdf.height)
}
######## Level ############
data <- read.csv("California.csv")
t <- data$year[data$state=="California"]
y <- data$cigsale[data$state=="California"]
x <- data[data$state!="California",1:3]
x <- as.matrix(reshape(x, timevar = "state", idvar="year", direction = "wide"))[,-1]
y <- y/100
x <- x/100
# cutoff: 1988
A <- y[t<=1988]
B <- x[t<=1988,]
C <- NULL
L <- 7
x.T <- x[(t>=1988+1&t<=1988+L),]
eq <- 1
lb <- 0
method.u <- "all"
alpha <- 0.1
M <- 100
result <- sc.pi(A=A, B=B, C=C, x.T=x.T, eq=eq, lb=lb, method.u=method.u, alpha=alpha, M=M, model=3, u.order=1, constant=F)
sc <- c(result$fit.pr, result$fit.po)*100
na <- rep(NA, nrow(B))
sc.l.0 <- c(na, result$sc.l.0)*100; sc.r.0 <- c(na, result$sc.r.0)*100
sc.l.1 <- c(na, result$sc.l.1)*100; sc.r.1 <- c(na, result$sc.r.1)*100
sc.l.2 <- c(na, result$sc.l.2)*100; sc.r.2 <- c(na, result$sc.r.2)*100
sc.l.3 <- c(na, result$sc.l.3)*100; sc.r.3 <- c(na, result$sc.r.3)*100
# Plot
dat <- data.frame(t=t[t<=1988+L], y=y[t<=1988+L]*100, sname="California")
dat.sc <- data.frame(t=t[t<=1988+L], sc=sc,
lb0=sc.l.0, ub0=sc.r.0,
lb1=sc.l.1, ub1=sc.r.1,
lb2=sc.l.2, ub2=sc.r.2,
lb3=sc.l.3, ub3=sc.r.3,
sname="SC unit")
pdf.width <- 6
pdf.height <- 4.5
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
labs(x="year", y="cigarette sales per capita (in packs)") +
theme(legend.position = c(0.15,0.15),
legend.background = element_rect(fill = "transparent"))
plot <- plot + geom_line(data=dat, aes(x=t, y=y, colour=sname), linetype=1) +
geom_point(data=dat, aes(x=t, y=y, colour=sname), shape=1) +
geom_line(data=dat.sc, aes(x=t, y=sc, colour=sname), linetype=5) +
geom_point(data=dat.sc, aes(x=t, y=sc, colour=sname), col="blue", shape=19) +
geom_vline(xintercept=1988, linetype="dotted") +
geom_text(aes(x=1988, label="\nProposition 99", y=120), angle=90, size=3.5)
plot0 <- plot + scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelCA-Tobacco_SC.pdf", width=pdf.width, height=pdf.height)
plot.w <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelCA-Tobacco_SCwithPI_w.pdf", width=pdf.width, height=pdf.height)
plot1 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb1, ymax=ub1),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelCA-Tobacco_SCwithPI_1.pdf", width=pdf.width, height=pdf.height)
plot2 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb2, ymax=ub2),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelCA-Tobacco_SCwithPI_2.pdf", width=pdf.width, height=pdf.height)
plot3 <- plot + geom_errorbar(data=dat.sc, aes(x=t, ymin=lb3, ymax=ub3),
col="maroon", width=0.5, linetype=5) +
geom_errorbar(data=dat.sc, aes(x=t, ymin=lb0, ymax=ub0),
col="blue", width=0.5, linetype=1) +
scale_color_manual(name="", values = c("black", "blue"),
guide=guide_legend(override.aes = list(
linetype=c(1,5), shape=c(1, 19))))
ggsave("figures/LevelCA-Tobacco_SCwithPI_3.pdf", width=pdf.width, height=pdf.height)
#### Sensitivity analysis #######
# Gaussian bound:
multi <- c(0.25, 0.5, 1, 1.5, 2)
time <- c(1989)
for (l in 1:length(time)) {
ssc.l.1 <- ssc.r.1 <- c()
u.mean <- result$u.1[time[l]-1988]
sig <- sqrt(result$u.2[time[l]-1988])
sig.seq <- multi*sig
for (s in 1:length(sig.seq)) {
eps <- sqrt(-log(alpha/4)*2*(sig.seq[s]^2))
ssc.l.1[s] <- sc.l.0[t==time[l]] + (u.mean - eps)*100
ssc.r.1[s] <- sc.r.0[t==time[l]] + (u.mean + eps)*100
}
sen.dat <- data.frame(t=c(1:5), lb1=ssc.l.1, ub1=ssc.r.1,
lb=rep(sc.l.0[t==time[l]], 5),
ub=rep(sc.r.0[t==time[l]], 5),
lab=as.factor(multi))
plot <- ggplot() + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="sd. of e", y="cigarette sales per capita (in packs)")
plot <- plot + geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb1, ymax=ub1),
col="maroon", width=0.2, linetype=5) +
geom_errorbar(data=sen.dat, aes(x=lab, ymin=lb, ymax=ub),
col="blue", width=0.2, linetype=1) +
geom_hline(yintercept = y[t==time[l]]*100, linetype=1, size=0.3, alpha=0.8) +
annotate("text", x=5.4, y=y[t==time[l]]*100-.4, label="Y(1)", size=3.5) +
scale_x_discrete(labels=c(parse(text=TeX("$0.25\\hat{\\sigma}$")),
parse(text=TeX("$0.5\\hat{\\sigma}$")),
parse(text=TeX("$\\hat{\\sigma}$")),
parse(text=TeX("$1.5\\hat{\\sigma}$")),
parse(text=TeX("$2\\hat{\\sigma}$"))))
ggsave(paste("figures/LevelCA_sens_", l, ".pdf", sep=""), width=pdf.width, height=pdf.height)
}