hazards$Date <- as.Date(as.character(hazards$Date),format="%Y%m%d")
## f hazard rates by group (12MA)
hazards_f <- melt(hazards[, c("Date", "f_m16to24", "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus")], id="Date")
g1 <- ggplot(hazards_f) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="f by education/age group (SA)") + theme_bw()
recessions.trim = subset(recessions.df, Peak >= min(hazards_12MA$Date) )
g1 = g1 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g1)
hazards_f <- melt(hazards[, c("Date", "f_m16to24", "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus", "f_act_ws", "f_act_total")], id="Date")
g2 <- ggplot(hazards_f) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="f by education/age group (SA)") + theme_bw() + scale_color_manual(values=c("gray", "gray", "gray", "gray", "gray", "gray", "blue", "orange"))
recessions.trim = subset(recessions.df, Peak >= min(hazards_12MA$Date) )
g2 = g2 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g2)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sexage.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
# "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus", "s_m16to24", "s_m25to54", "s_m55plus", "s_f16to24", "s_f25to54", "s_f55plus"
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sexage_sa.csv")
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sexage", echo = FALSE) # set path
## s hazard rates by group (12MA)
hazards_s <- melt(hazards[, c("Date", "s_m16to24", "s_m25to54", "s_m55plus", "s_f16to24", "s_f25to54", "s_f55plus")], id="Date")
g1 <- ggplot(hazards_s) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="s by gender/age group (SA)") + theme_bw()
recessions.trim = subset(recessions.df, Peak >= min(hazards$Date) )
g1 = g1 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g1)
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sexage", echo = FALSE) # set path
library(knitr)
library(ggplot2)
library(reshape2)
recessions.df = read.table(textConnection(
"Peak, Trough
1857-06-01, 1858-12-01
1860-10-01, 1861-06-01
1865-04-01, 1867-12-01
1869-06-01, 1870-12-01
1873-10-01, 1879-03-01
1882-03-01, 1885-05-01
1887-03-01, 1888-04-01
1890-07-01, 1891-05-01
1893-01-01, 1894-06-01
1895-12-01, 1897-06-01
1899-06-01, 1900-12-01
1902-09-01, 1904-08-01
1907-05-01, 1908-06-01
1910-01-01, 1912-01-01
1913-01-01, 1914-12-01
1918-08-01, 1919-03-01
1920-01-01, 1921-07-01
1923-05-01, 1924-07-01
1926-10-01, 1927-11-01
1929-08-01, 1933-03-01
1937-05-01, 1938-06-01
1945-02-01, 1945-10-01
1948-11-01, 1949-10-01
1953-07-01, 1954-05-01
1957-08-01, 1958-04-01
1960-04-01, 1961-02-01
1969-12-01, 1970-11-01
1973-11-01, 1975-03-01
1980-01-01, 1980-07-01
1981-07-01, 1982-11-01
1990-07-01, 1991-03-01
2001-03-01, 2001-11-01
2007-12-01, 2009-06-01"), sep=',',
colClasses=c('Date', 'Date'), header=TRUE)
# import data
hazards <- read.csv("hazards_sexage_sa.csv",header=TRUE)
hazards$Date <- as.Date(as.character(hazards$Date),format="%Y%m%d")
## f hazard rates by group (12MA)
hazards_f <- melt(hazards[, c("Date", "f_m16to24", "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus")], id="Date")
g1 <- ggplot(hazards_f) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="f by education/age group (SA)") + theme_bw()
recessions.trim = subset(recessions.df, Peak >= min(hazards$Date) )
g1 = g1 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g1)
hazards_f <- melt(hazards[, c("Date", "f_m16to24", "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus", "f_act_ws", "f_act_total")], id="Date")
g2 <- ggplot(hazards_f) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="f by education/age group (SA)") + theme_bw() + scale_color_manual(values=c("gray", "gray", "gray", "gray", "gray", "gray", "blue", "orange"))
recessions.trim = subset(recessions.df, Peak >= min(hazards_12MA$Date) )
g2 = g2 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g2)
## s hazard rates by group (12MA)
hazards_s <- melt(hazards[, c("Date", "s_m16to24", "s_m25to54", "s_m55plus", "s_f16to24", "s_f25to54", "s_f55plus")], id="Date")
g1 <- ggplot(hazards_s) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="s by gender/age group (SA)") + theme_bw()
recessions.trim = subset(recessions.df, Peak >= min(hazards$Date) )
g1 = g1 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g1)
hazards_s <- melt(hazards[, c("Date", "s_m16to24", "s_m25to54", "s_m55plus", "s_f16to24", "s_f25to54", "s_f55plus", "s_act_ws", "s_act_total")], id="Date")
g2 <- ggplot(hazards_s) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="s by gender/age group (SA)") + theme_bw() + scale_color_manual(values=c("gray", "gray", "gray", "gray", "gray", "gray", "blue", "orange"))
recessions.trim = subset(recessions.df, Peak >= min(hazards$Date) )
g2 = g2 + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='gray', alpha=0.2) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
plot(g2)
install.packages("zoo")
hazards_f <- melt(hazards[, c("Date", "f_m16to24", "f_m25to54", "f_m55plus", "f_f16to24", "f_f25to54", "f_f55plus")], id="Date")
View(hazards_f)
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sex", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "hazards_sex_sa_quart.csv", row.names=FALSE)
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sex", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
View(weights)
View(weights)
View(hazards)
View(weights)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12, x11="")))
}
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12), x11=""))
}
View(weights)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
View(weights)
View(weights)
datalist = list()
for(i in 2:3){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
datalist = list()
for(i in 2:3){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights)
datalist = list()
for(i in 2:4){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
datalist = list()
for(i in 2:6){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
View(weights)
for(i in 7){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
View(weights)
for(i in 6){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "hazards_sex_sa_quart.csv", row.names=FALSE)
install.packages("seasonalview")
View(weights)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights)
write.csv(weights_sa.df, "weights_sex_sa.csv", row.names=FALSE)
View(weights)
View(hazards_sa.df)
View(weights)
View(hazards_sa.df)
View(weights_sa.df)
View(weights)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
stock <- read.csv("stock_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(stock)){
datalist[[i]] <- final(seas(ts(stock[ ,i], start = c(1976, 1), frequency = 12)))
}
stock_sa_0 = do.call(rbind, datalist)
stock_sa <- cbind(as.matrix(stock$Date), t(stock_sa_0))
stock_sa.df <- as.data.frame(stock_sa)
colnames(stock_sa.df) <- colnames(stock)
write.csv(stock_sa.df, "stock_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "hazards_sex_sa_quart.csv", row.names=FALSE)
View(stock)
datalist = list()
for(i in 2:ncol(stock)){
datalist[[i]] <- final(seas(ts(stock[ ,i], start = c(1976, 1), frequency = 12)))
}
stock_sa_0 = do.call(rbind, datalist)
stock_sa <- cbind(as.matrix(stock$Date), t(stock_sa_0))
stock_sa.df <- as.data.frame(stock_sa)
colnames(stock_sa.df) <- colnames(stock)
write.csv(stock_sa.df, "stock_sex_sa.csv", row.names=FALSE)
Utotal <- stock_sa.df$U_men+stock_sa.df$U_women
Ntotal <- stock_sa.df$N_men+stock_sa.df$N_women
Etotal <- stock_sa.df$E_men+stock_sa.df$E_women
sw_men <- (stock_sa.df$E_men+stock_sa.df$N_men)/(Etotal+Ntotal)
View(weights_sa)
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sex", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
stock <- read.csv("stock_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(stock)){
datalist[[i]] <- final(seas(ts(stock[ ,i], start = c(1976, 1), frequency = 12)))
}
stock_sa_0 = do.call(rbind, datalist)
stock_sa <- cbind(as.matrix(stock$Date), t(stock_sa_0))
stock_sa.df <- as.data.frame(stock_sa)
Utotal <- stock_sa.df$U_men+stock_sa.df$U_women
Ntotal <- stock_sa.df$N_men+stock_sa.df$N_women
Etotal <- stock_sa.df$E_men+stock_sa.df$E_women
sw_men <- (stock_sa.df$E_men+stock_sa.df$N_men)/(Etotal+Ntotal)
colnames(stock_sa.df) <- colnames(stock)
write.csv(stock_sa.df, "stock_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "hazards_sex_sa_quart.csv", row.names=FALSE)
View(weights)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
stock <- read.csv("stock_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
weights <- read.csv("weight_sex.csv",header=TRUE)
stock <- read.csv("stock_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "hazards_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "hazards_sex_sa_quart.csv", row.names=FALSE)
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights)
View(weights)
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[,2:5])
write.csv(weights_sa.df, "weights_sex_sa.csv", row.names=FALSE)
View(weights_sa.df)
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[,1:5])
write.csv(weights_sa.df, "weights_sex_sa.csv", row.names=FALSE)
View(weights_sa.df)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:5){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[,1:5])
write.csv(weights_sa.df, "weights_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights_sa.df)){
datalist[[i]] <- aggregate(ts(weights_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_sa_quart_0 = do.call(rbind, datalist)
weights_sa_quart <- cbind(as.matrix(time), t(weights_sa_quart_0))
weights_sa_quart.df <- as.data.frame(weights_sa_quart)
colnames(weights_sa_quart.df) <- colnames(weights_sa.df)
write.csv(weights_sa_quart.df, "weights_sex_sa_quart.csv", row.names=FALSE)
knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sex", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("hazards_sex.csv",header=TRUE)
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_sex.csv",header=TRUE)
weights <- read.csv("csv/weight_sex.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "csv/hazards_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "csv/hazards_sex_sa_quart.csv", row.names=FALSE)
