Mitsuo Shiota 2019-03-19
- Summary
- Libraries
- Self-made tqr package
- Get data from FRED
- Transform data
- Get data from Atlanta Fed Wage Growth Trucker
- Combine data
- Prepare for the plot
- Dashboard
Updated: 2024-12-07
This is a pilot project to utilize R language and tidyquant package for economic analysis.
I read the bloomberg article, “Yellen’s Labor Market Dashboard”, and found 12 time-series data, central bankers are supposed to watch. I decided to replicate this dashboard. I added PCE, excl food and energy, to see price stability. Update: I also added prime age employment rate, as it is said to be more closely related to wage than headline (un)employment rate.
I use tq_get function from tidyquant package to get tidy data from Federal Reserve Bank of St. Louis (FRED). And some of my self-made functions depend on tidyquant package.
I use GET function from httr package to get an excel file from Federal Reserve Bank of Atlanta (Wage Growth Tracker), and readxl package to read excel data.
library(tidyquant)
library(tidyverse)
library(httr)
library(readxl)tidyquant package assumes tidy data, basically a data frame of long format, of 3 columns: “date”, “symbol” and “price.” These column names come from stock price analysis. I use self-made tqr package to transform values in price column.
library(tsibble)
library(tqr)At FRED, I searched data, and got symbols from the parentheses in the title. For example, if the title is “Japan / U.S. Foreign Exchange Rate (AEXJPUS)”, “AEXJPUS” is the symbol.
yellen_labor_mkt_symbols <- c(
"UNRATE", # Unemployment rate
"U6RATE", # U-6, underemployment rate
"LNS13025703", # Long-term unemployed share
"PAYEMS", # Non-farm payrolls, total
"JTSJOR", # Job openings rate
"JTSLDR", # Layoffs/discharged rate
"JTSQUR", # Quits rate
"JTSHIR", # Hires rate
"CEU0500000003", # Average hourly earnings, total private
"ECIALLCIV", # Employment cost index, all civilians, quaterly
"CIVPART", # Labor force participation rate
"LREM25TTUSM156S", # Employment Rate: Aged 25-54: All Persons
"PCEPILFE" # PCE excluding food and energy
) I set the first date to get data as START. I also set XLIM, x axis limits, to plot later.
START = "2006-01-01"
XLIM <- c(as.Date("2008-01-01"), as.Date("2024-12-01"))Now I can use tq_get function from tidyquant package to download data from FRED.
labor_mkt_all <- yellen_labor_mkt_symbols %>%
tq_get(get = "economic.data", from = START)Employment cost index is the only quarterly data, and all the others are monthly data. I transform Employment cost index to year-over-year growth rates, and all the others to tsibble (tbl_ts class).
# transform Employment cost index, quarterly data to YoY
eci <- labor_mkt_all %>%
filter(symbol == "ECIALLCIV") %>%
mutate(date = yearquarter(date)) %>%
as_tsibble(key = "symbol", index = "date") %>%
tq_gr(n = 4) %>%
mutate(date = as.Date(date))
# Others are monthly data, transformed to tsibble
labor_mkt_m <- labor_mkt_all %>%
filter(symbol != "ECIALLCIV") %>%
mutate(date = yearmonth(date)) %>%
as_tsibble(key = "symbol", index = "date")Non-farm payrolls requires the most complicated transformation. I have to take differences from the prior month, and make them 3 month moving average. Fortunately pipe operator helps me.
# transform Non-farm payrolls, total to differences, 3 month moving average
payems <- labor_mkt_m %>%
filter(symbol == "PAYEMS") %>%
tq_diff() %>%
tq_ma(n = 3)I transform PCE to year-over-year growth rates, and average hourly earnings to year-over-year growth rates and 3 month moving average.
# transform PCE to growth rates, YoY
pce <- labor_mkt_m %>%
filter(symbol == "PCEPILFE") %>%
tq_gr(n = 12)
# transform Average hourly earnings to YoY, 3 ma
ceu <- labor_mkt_m %>%
filter(symbol == "CEU0500000003") %>%
tq_gr(n = 12) %>%
tq_ma(n = 3)There is one datum I can’t get from FRED, that is Atlanta Fed Wage Tracker. Unfortunately I could not find API to get data. So, initially I manually downloaded an excel file, transformed it into a csv file, and used read_csv to read.
Later, I found “Read Excel file from a URL using the readxl package” in StackOverflow. Now, I automate the reading process, though it is awkward. If Atlanta Fed changes something, this part will fail.
url <- "https://www.frbatlanta.org/-/media/documents/datafiles/chcs/wage-growth-tracker/wage-growth-data.xlsx"
GET(url, write_disk(tf <- tempfile(fileext = ".xlsx")))res <- read_excel(tf, col_names = FALSE, na = c(".", ""), skip = 2)
names(res)[1:2] <- c("date", "price")
res$date <- as.Date(res$date)
wage_tracker <- res %>%
select(date, price)
wage_tracker$symbol <- "WAGETR"
wage_tracker <- wage_tracker %>%
filter(date >= START)I combine transformed data with not-transformed data, and get tidy data.
As bind_rows function drops index necessary to tbl_ts class, I have to
transform tsibble to tibble.
labor_mkt <- labor_mkt_m %>%
filter(!(symbol %in% c("PAYEMS", "PCEPILFE", "CEU0500000003"))) %>%
bind_rows(payems, pce, ceu, eci, wage_tracker) %>%
as_tibble()I change class of symbol from character to factor in the appropriate order for the plot.
labor_mkt$symbol <- factor(labor_mkt$symbol,
levels = c("UNRATE", "LNS13025703", "U6RATE", "CIVPART",
"JTSJOR", "JTSLDR", "JTSQUR", "JTSHIR",
"PAYEMS", "CEU0500000003", "ECIALLCIV", "WAGETR",
"LREM25TTUSM156S", "PCEPILFE"))I recode symbol to make them more readable in the plot.
labor_mkt <- labor_mkt %>%
mutate(
symbol = fct_recode(symbol,
"Unemployment rate" = "UNRATE",
"Long-term unemployed share" = "LNS13025703",
"U-6 underemployment rate" = "U6RATE",
"Labor force participation rate" = "CIVPART",
"Job openings rate" = "JTSJOR",
"Layoffs/discharged rate" = "JTSLDR",
"Quits rate" = "JTSQUR",
"Hires rate" = "JTSHIR",
"Non-farm payroll increase, 3 mo avg" = "PAYEMS",
"Avg hourly earnings, YoY, 3 mo avg" = "CEU0500000003",
"Employment cost index, YoY, quarterly" = "ECIALLCIV",
"Atlanta Fed wage tracker, YoY, 3 mo avg" = "WAGETR",
"Employment Rate: Aged 25-54" = "LREM25TTUSM156S",
"PCE excluding food and energy, YoY" = "PCEPILFE"
)
)OK. Let us plot. I add the labels at the latest. Some of them are put ugly, but useful enough.
labor_mkt %>%
group_by(symbol) %>%
mutate(
label = if_else(date == max(date),
paste(lubridate::month(date, label = TRUE, abbr = TRUE),
as.character(round(price, 2))
, sep = " "),
NA_character_)
) %>%
ungroup() %>%
ggplot(aes(x = date, y = price)) +
geom_line() +
facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
geom_text(aes(label = label), na.rm = TRUE,
hjust = 1, vjust = 0) +
coord_cartesian(xlim = XLIM) +
labs(
title = "Yellen's US labor market dashboard",
x = "",
y = "")
Save plot in output/Yellen.pdf.
ggsave(filename = "output/Yellen.pdf",
width = 8, height = 16, units = "in", dpi = 300)EOL