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_age.csv",header=TRUE)
hazards$Date <- as.Date(as.character(hazards$Date),format="%Y%m%d")
hazards_12MA <- read.csv("hazards_age_12MA.csv",header=TRUE)
hazards_12MA$Date <- as.Date(as.character(hazards_12MA$Date),format="%Y%m%d")
sweight <- read.csv("sweight_age.csv",header=TRUE)
sweight$Date <- as.Date(as.character(sweight$Date),format="%Y%m%d")
sweight_12MA <- read.csv("sweight_age_12MA.csv",header=TRUE)
sweight_12MA$Date <- as.Date(as.character(sweight_12MA$Date),format="%Y%m%d")
hazards_f <- melt(hazards[, c("Date", "f16to24", "f25to54", "f55plus")], id="Date")
g <- ggplot(hazards_f) + geom_line(aes(x=Date, y=value, color=variable)) + labs(title="f by age group (raw)") + theme_bw()
recessions.trim = subset(recessions.df, Peak >= min(hazards_age$Date) )
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
# you can make sure you are in the right directory
print( getwd() )
knitr::opts_knit$set(root.dir = dirname(current_path), echo = FALSE) # set path
#knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/age", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_age.csv",header=TRUE)
weights <- read.csv("csv/weights_age.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_age_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_age_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:11){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
View(weights)
View(weights)
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
# you can make sure you are in the right directory
print( getwd() )
knitr::opts_knit$set(root.dir = dirname(current_path), echo = FALSE) # set path
#knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/age", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_age.csv",header=TRUE)
weights <- read.csv("csv/weights_age.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$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "csv/hazards_age_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "csv/hazards_age_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:7){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[1:7])
write.csv(weights_sa.df, "csv/weights_age_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, "csv/weights_age_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 8:8){
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$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[8:8])
write.csv(weights_sa.df, "csv/weights_age_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights_sa.df)){
datalist[[i]] <- aggregate(ts(weights_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_sa_quart_0 = do.call(rbind, datalist)
weights_sa_quart <- cbind(as.matrix(time), t(weights_sa_quart_0))
weights_sa_quart.df <- as.data.frame(weights_sa_quart)
colnames(weights_sa_quart.df) <- colnames(weights_sa.df)
write.csv(weights_sa_quart.df, "csv/weights_age_sa_quart.csv", row.names=FALSE)
View(weights_sa.df)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 9:9){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[9:9])
write.csv(weights_sa.df, "csv/weights_age_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, "csv/weights_age_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 10:10){
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$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[10:10])
write.csv(weights_sa.df, "csv/weights_age_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights_sa.df)){
datalist[[i]] <- aggregate(ts(weights_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_sa_quart_0 = do.call(rbind, datalist)
weights_sa_quart <- cbind(as.matrix(time), t(weights_sa_quart_0))
weights_sa_quart.df <- as.data.frame(weights_sa_quart)
colnames(weights_sa_quart.df) <- colnames(weights_sa.df)
write.csv(weights_sa_quart.df, "csv/weights_age_sa_quart.csv", row.names=FALSE)
View(weights_sa.df)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_age_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:10){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[1:10])
write.csv(weights_sa.df, "csv/weights_age_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, "csv/weights_age_sa_quart.csv", row.names=FALSE)
View(weights_sa.df)
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
# you can make sure you are in the right directory
print( getwd() )
knitr::opts_knit$set(root.dir = dirname(current_path), echo = FALSE) # set path
#knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/sex", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_sex.csv",header=TRUE)
weights <- read.csv("csv/weights_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$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "csv/hazards_sex_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "csv/hazards_sex_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_sex_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:7){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[1:7])
write.csv(weights_sa.df, "csv/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, "csv/weights_sex_sa_quart.csv", row.names=FALSE)
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
# you can make sure you are in the right directory
print( getwd() )
knitr::opts_knit$set(root.dir = dirname(current_path), echo = FALSE) # set path
#knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/age", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_ageteen.csv",header=TRUE)
weights <- read.csv("csv/weights_ageteen.csv",header=TRUE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:ncol(hazards)){
datalist[[i]] <- final(seas(ts(hazards[ ,i], start = c(1976, 1), frequency = 12)))
}
hazards_sa_0 = do.call(rbind, datalist)
hazards_sa <- cbind(as.matrix(hazards$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_ageteen_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_ageteen_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_ageteen_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:13){
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$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[1:13])
write.csv(weights_sa.df, "csv/weights_ageteen_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights_sa.df)){
datalist[[i]] <- aggregate(ts(weights_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_sa_quart_0 = do.call(rbind, datalist)
weights_sa_quart <- cbind(as.matrix(time), t(weights_sa_quart_0))
weights_sa_quart.df <- as.data.frame(weights_sa_quart)
colnames(weights_sa_quart.df) <- colnames(weights_sa.df)
write.csv(weights_sa_quart.df, "csv/weights_ageteen_sa_quart.csv", row.names=FALSE)
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path))
# you can make sure you are in the right directory
print( getwd() )
knitr::opts_knit$set(root.dir = dirname(current_path), echo = FALSE) # set path
#knitr::opts_knit$set(root.dir = "/Volumes/Jin/CPS/unemp_count_Nov2018/findhaz/age", echo = FALSE) # set path
library(foreign)
library(seasonal)
# import data
hazards <- read.csv("csv/hazards_ageteen.csv",header=TRUE)
weights <- read.csv("csv/weights_ageteen.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$Date), t(hazards_sa_0))
hazards_sa.df <- as.data.frame(hazards_sa)
colnames(hazards_sa.df) <- colnames(hazards)
write.csv(hazards_sa.df, "csv/hazards_ageteen_sa.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(hazards_sa.df)){
datalist[[i]] <- aggregate(ts(hazards_sa.df[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
hazards_sa_quart_0 = do.call(rbind, datalist)
hazards_sa_quart <- cbind(as.matrix(time), t(hazards_sa_quart_0))
hazards_sa_quart.df <- as.data.frame(hazards_sa_quart)
colnames(hazards_sa_quart.df) <- colnames(hazards_sa.df)
write.csv(hazards_sa_quart.df, "csv/hazards_ageteen_sa_quart.csv", row.names=FALSE)
# make quarterly data
datalist = list()
for(i in 2:ncol(weights)){
datalist[[i]] <- aggregate(ts(weights[ ,i], start = c(1976, 1), frequency = 12), nfrequency=4, mean)
}
time <- time(datalist[[2]])
weights_quart_0 = do.call(rbind, datalist)
weights_quart <- cbind(as.matrix(time), t(weights_quart_0))
weights_quart.df <- as.data.frame(weights_quart)
colnames(weights_quart.df) <- colnames(weights)
write.csv(weights_quart.df, "csv/weights_ageteen_nsa_quart.csv", row.names=FALSE)
# seasonal adjustment: X-13ARIMA-SEATS
datalist = list()
for(i in 2:13){
datalist[[i]] <- final(seas(ts(weights[ ,i], start = c(1976, 1), frequency = 12)))
}
weights_sa_0 = do.call(rbind, datalist)
weights_sa <- cbind(as.matrix(weights$Date), t(weights_sa_0))
weights_sa.df <- as.data.frame(weights_sa)
colnames(weights_sa.df) <- colnames(weights[1:13])
write.csv(weights_sa.df, "csv/weights_ageteen_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, "csv/weights_ageteen_sa_quart.csv", row.names=FALSE)
