low_65_predict = c(low_65_series$index, low_65_predict)
ggplot(data = v, aes(x = DATE, y = index, col = variable)) +
geom_path() +
theme(
legend.position = "none"
)
series = unique(v$variable)
i = 1
sub  = v[variable == series[i]]
View(sub)
y.max = tail(sub$DATE,1)
View(v)
low.gr = v[, min(rate), by = DATE]
low.gr = v[, min(rate), by = DATE]
rate.sub = low.gr[DATE >= y.max]
View(rate.sub)
library(data.table)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(matrixStats)
library(reshape2)
text.size = 10
setwd("/home/blair/Desktop/Empirical Research/BLS_prices")
d = fread("prices.csv")
d$name = factor(d$name)
d$name =  reorder(d$name, -d$index, mean)
p.log = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous(breaks = seq(1900, 2060, 20)) +
scale_y_log10("Price Index (1935 = 1)") +
ggtitle("log scale") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position =  "none",
plot.title = element_text(face = "bold", hjust = 0.5, vjust = -2),
axis.line = element_line(color = "black"),
axis.title = element_blank(),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size*0.8, family="Times"))
p = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous("", breaks = seq(1900, 2050, 10)) +
scale_y_continuous("Price Index (1935 = 1)") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.text = element_text(size = rel(0.7)),
legend.key.height = unit(0.6, "cm"),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x= element_text(vjust=-0.4, size=rel(0.9)),
axis.title.y = element_text(vjust= 1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
inset = ggplotGrob(p.log)
p = p + annotation_custom(grob = inset, xmin = 1930, xmax = 1980, ymin = 25, ymax = 53)
# gdp
################################################################################
setwd("/home/blair/Desktop/Empirical Research/GDP/Vintage")
v = fread("routputMvQd.csv")
m = 1947 + 284/4
y = seq(1947, m - 0.25, 0.25)
v$DATE = y
v = melt(v, id.vars= "DATE")
v$value = gsub("#N/A","", v$value)
v$value = as.numeric(v$value)
v$variable = as.character(v$variable)
f = function(x){x/x[1]}
index = v[, .(index = f(value)), by = variable]
v$index = index$index
v = na.omit(v)
gr = function(x){
d = rev(end(x))
g = x[ -d[1]] / x[ -d[2] ]
g = c(1, g)
return(g)
}
rate = v[, gr(value), by = variable]
v$rate = rate$V1
# low growth rate
low.gr = v[, min(rate), by = DATE]
series = unique(v$variable)
l = length(series)
i = 1
sub  = v[variable == series[i]]
View(sub)
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE >= y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
y = unique(v$DATE)
predict = c(sub$index, predict)
plot(predict)
View(sub)
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE >= y.max]
View(rate.sub)
sub
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE > y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
predict = c(sub$index, predict)
output = data.table(series = series[i], y, predict)
library(data.table)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(matrixStats)
library(reshape2)
text.size = 10
setwd("/home/blair/Desktop/Empirical Research/BLS_prices")
d = fread("prices.csv")
d$name = factor(d$name)
d$name =  reorder(d$name, -d$index, mean)
p.log = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous(breaks = seq(1900, 2060, 20)) +
scale_y_log10("Price Index (1935 = 1)") +
ggtitle("log scale") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position =  "none",
plot.title = element_text(face = "bold", hjust = 0.5, vjust = -2),
axis.line = element_line(color = "black"),
axis.title = element_blank(),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size*0.8, family="Times"))
p = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous("", breaks = seq(1900, 2050, 10)) +
scale_y_continuous("Price Index (1935 = 1)") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.text = element_text(size = rel(0.7)),
legend.key.height = unit(0.6, "cm"),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x= element_text(vjust=-0.4, size=rel(0.9)),
axis.title.y = element_text(vjust= 1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
inset = ggplotGrob(p.log)
p = p + annotation_custom(grob = inset, xmin = 1930, xmax = 1980, ymin = 25, ymax = 53)
# gdp
################################################################################
setwd("/home/blair/Desktop/Empirical Research/GDP/Vintage")
v = fread("routputMvQd.csv")
m = 1947 + 284/4
y = seq(1947, m - 0.25, 0.25)
v$DATE = y
v = melt(v, id.vars= "DATE")
v$value = gsub("#N/A","", v$value)
v$value = as.numeric(v$value)
v$variable = as.character(v$variable)
f = function(x){x/x[1]}
index = v[, .(index = f(value)), by = variable]
v$index = index$index
v = na.omit(v)
gr = function(x){
d = rev(end(x))
g = x[ -d[1]] / x[ -d[2] ]
g = c(1, g)
return(g)
}
rate = v[, gr(value), by = variable]
v$rate = rate$V1
# low growth rate
low.gr = v[, min(rate), by = DATE]
series = unique(v$variable)
l = length(series)
y = unique(v$DATE)
final = NULL
for(i in 1:l){
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE > y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
predict = c(sub$index, predict)
output = data.table(series = series[i], y, predict)
final = rbind(final, output)
}
View(final)
ggplot() +
geom_path(final = v, aes(x = y, y = predict, col = series)) +
theme(
legend.position = "none"
)
ggplot() +
geom_path(data = final, aes(x = y, y = predict, col = series)) +
theme(
legend.position = "none"
)
ggplot() +
geom_path(data = final, aes(x = y, y = predict, col = series)) +
theme(
legend.position = "none"
)
output = data.table(series = series[i], year = y, predict)
library(data.table)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(matrixStats)
library(reshape2)
text.size = 10
setwd("/home/blair/Desktop/Empirical Research/BLS_prices")
d = fread("prices.csv")
d$name = factor(d$name)
d$name =  reorder(d$name, -d$index, mean)
p.log = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous(breaks = seq(1900, 2060, 20)) +
scale_y_log10("Price Index (1935 = 1)") +
ggtitle("log scale") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position =  "none",
plot.title = element_text(face = "bold", hjust = 0.5, vjust = -2),
axis.line = element_line(color = "black"),
axis.title = element_blank(),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size*0.8, family="Times"))
p = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous("", breaks = seq(1900, 2050, 10)) +
scale_y_continuous("Price Index (1935 = 1)") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.text = element_text(size = rel(0.7)),
legend.key.height = unit(0.6, "cm"),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x= element_text(vjust=-0.4, size=rel(0.9)),
axis.title.y = element_text(vjust= 1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
inset = ggplotGrob(p.log)
p = p + annotation_custom(grob = inset, xmin = 1930, xmax = 1980, ymin = 25, ymax = 53)
# gdp
################################################################################
setwd("/home/blair/Desktop/Empirical Research/GDP/Vintage")
v = fread("routputMvQd.csv")
m = 1947 + 284/4
y = seq(1947, m - 0.25, 0.25)
v$DATE = y
v = melt(v, id.vars= "DATE")
v$value = gsub("#N/A","", v$value)
v$value = as.numeric(v$value)
v$variable = as.character(v$variable)
f = function(x){x/x[1]}
index = v[, .(index = f(value)), by = variable]
v$index = index$index
v = na.omit(v)
gr = function(x){
d = rev(end(x))
g = x[ -d[1]] / x[ -d[2] ]
g = c(1, g)
return(g)
}
rate = v[, gr(value), by = variable]
v$rate = rate$V1
# low growth rate
low.gr = v[, min(rate), by = DATE]
series = unique(v$variable)
l = length(series)
y = unique(v$DATE)
final = NULL
for(i in 1:l){
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE > y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
predict = c(sub$index, predict)
output = data.table(series = series[i], year = y, predict)
final = rbind(final, output)
}
ggplot() +
geom_path(data = final, aes(x = year, y = predict, col = series)) +
theme(
legend.position = "none"
)
ggplot() +
geom_path(data = final, aes(x = year, y = predict)) +
theme(
legend.position = "none"
)
library(data.table)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(matrixStats)
library(reshape2)
text.size = 10
setwd("/home/blair/Desktop/Empirical Research/BLS_prices")
d = fread("prices.csv")
d$name = factor(d$name)
d$name =  reorder(d$name, -d$index, mean)
p.log = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous(breaks = seq(1900, 2060, 20)) +
scale_y_log10("Price Index (1935 = 1)") +
ggtitle("log scale") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position =  "none",
plot.title = element_text(face = "bold", hjust = 0.5, vjust = -2),
axis.line = element_line(color = "black"),
axis.title = element_blank(),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size*0.8, family="Times"))
p = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous("", breaks = seq(1900, 2050, 10)) +
scale_y_continuous("Price Index (1935 = 1)") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.text = element_text(size = rel(0.7)),
legend.key.height = unit(0.6, "cm"),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x= element_text(vjust=-0.4, size=rel(0.9)),
axis.title.y = element_text(vjust= 1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
inset = ggplotGrob(p.log)
p = p + annotation_custom(grob = inset, xmin = 1930, xmax = 1980, ymin = 25, ymax = 53)
# gdp
################################################################################
setwd("/home/blair/Desktop/Empirical Research/GDP/Vintage")
v = fread("routputMvQd.csv")
m = 1947 + 284/4
y = seq(1947, m - 0.25, 0.25)
v$DATE = y
v = melt(v, id.vars= "DATE")
v$value = gsub("#N/A","", v$value)
v$value = as.numeric(v$value)
v$variable = as.character(v$variable)
f = function(x){x/x[1]}
index = v[, .(index = f(value)), by = variable]
v$index = index$index
v = na.omit(v)
gr = function(x){
d = rev(end(x))
g = x[ -d[1]] / x[ -d[2] ]
g = c(1, g)
return(g)
}
rate = v[, gr(value), by = variable]
v$rate = rate$V1
# low growth rate
low.gr = v[, min(rate), by = DATE]
series = unique(v$variable)
l = length(series)
y = unique(v$DATE)
final = NULL
for(i in 1:l){
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE > y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
predict = c(sub$index, predict)
output = data.table(series = series[i], year = y, gdp = predict)
final = rbind(final, output)
}
ggplot() +
geom_path(data = final, aes(x = year, y = gdp)) +
theme(
legend.position = "none"
)
View(final)
ggplot() +
geom_path(data = final, aes(x = year, y = gdp)) +
theme(
legend.position = "none"
)
library(data.table)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(matrixStats)
library(reshape2)
text.size = 10
setwd("/home/blair/Desktop/Empirical Research/BLS_prices")
d = fread("prices.csv")
d$name = factor(d$name)
d$name =  reorder(d$name, -d$index, mean)
p.log = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous(breaks = seq(1900, 2060, 20)) +
scale_y_log10("Price Index (1935 = 1)") +
ggtitle("log scale") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position =  "none",
plot.title = element_text(face = "bold", hjust = 0.5, vjust = -2),
axis.line = element_line(color = "black"),
axis.title = element_blank(),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size*0.8, family="Times"))
p = ggplot() +
geom_line(data = d, aes(x  = year, y = index, col = name)) +
scale_x_continuous("", breaks = seq(1900, 2050, 10)) +
scale_y_continuous("Price Index (1935 = 1)") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.text = element_text(size = rel(0.7)),
legend.key.height = unit(0.6, "cm"),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x= element_text(vjust=-0.4, size=rel(0.9)),
axis.title.y = element_text(vjust= 1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,3,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
inset = ggplotGrob(p.log)
p = p + annotation_custom(grob = inset, xmin = 1930, xmax = 1980, ymin = 25, ymax = 53)
# gdp
################################################################################
setwd("/home/blair/Desktop/Empirical Research/GDP/Vintage")
v = fread("routputMvQd.csv")
m = 1947 + 284/4
y = seq(1947, m - 0.25, 0.25)
v$DATE = y
v = melt(v, id.vars= "DATE")
v$value = gsub("#N/A","", v$value)
v$value = as.numeric(v$value)
v$variable = as.character(v$variable)
f = function(x){x/x[1]}
index = v[, .(index = f(value)), by = variable]
v$index = index$index
v = na.omit(v)
gr = function(x){
d = rev(end(x))
g = x[ -d[1]] / x[ -d[2] ]
g = c(1, g)
return(g)
}
rate = v[, gr(value), by = variable]
v$rate = rate$V1
# low growth rate
low.gr = v[, min(rate), by = DATE]
series = unique(v$variable)
l = length(series)
y = unique(v$DATE)
final = NULL
for(i in 1:l){
sub  = v[variable == series[i]]
y.max = tail(sub$DATE,1)
rate.sub = low.gr[DATE > y.max]
predict = cumprod(rate.sub$V1)*tail(sub$index,1)
predict = c(sub$index, predict)
output = data.table(series = series[i], year = y, gdp = predict)
final = rbind(final, output)
}
ggplot() +
geom_path(data = final, aes(x = year, y = gdp)) +
theme(
legend.position = "none"
)
plot(final$year, final$gdp)
ggplot() +
geom_path(data = final, aes(x = year, y = gdp)) +
theme(
legend.position = "none"
)
ggplot() +
geom_path(data = final, aes(x = year, y = gdp, col = series)) +
theme(
legend.position = "none"
)
setwd("~/Desktop/Empirical Research/GDP/Vintage")
