Inflation Monitor
by : Chris at Savvy Analytics
Background : As we exit the recession driven by Covid-19, there has been a lot of talk about high inflation. Some inflation is probably inevitable with all of the various stimulus programs in 2020 and 2021. However, nearly all of the comparisons we hear on the news are measuring back to the trough of the recession in 2020.
Comparisons to that trough are naturally skewed. A better comparison would be to look at the annualized change over a longer period like 24 or 36 months.
Note : This article, like most of mine on R-Pubs, is both instructional and investigative. You can re-create any of these charts and tables from the embedded R code. Click the “Code” button to view.
# Clear Environment and load libraries
# Optional memory clear
rm(list=ls())
# Disable Scientific Notation in printing
options(scipen=999)
# Load libraries
require(tidyverse)
require(quantmod)
require(lubridate)
require(scales)
require(zoo)
require(grid)
require(DBI)
require(bigrquery)
# Load data using the QuantMod package to retrive historical data
# Minimum Wage and CPI data are provided by the St. Louis Federal Reserve "FRED" system
# Call use QuantMod's API into FRED
invisible(
getSymbols(c("CPIAUCNS", "CPILFENS", "PCE", "PPIFIS"), src = "FRED")
)
#PPIACO
# Combine the time series data into a data frame
PresentationDF <- data.frame(CPILFENS) %>%
rename(CPICore = 1) %>%
mutate(QuoteDate = as.Date(row.names(.))) %>%
left_join(data.frame(CPIAUCNS) %>%
rename(CPI = 1) %>%
mutate(QuoteDate = as.Date(row.names(.))),
by = c("QuoteDate")) %>%
left_join(data.frame(PCE) %>%
rename(PCE = 1) %>%
mutate(QuoteDate = as.Date(row.names(.))),
by = c("QuoteDate")) %>%
left_join(data.frame(PPIFIS) %>%
rename(PPI = 1) %>%
mutate(QuoteDate = as.Date(row.names(.))),
by = c("QuoteDate")) %>%
# filter(complete.cases(.)) %>%
mutate(CPI3MRunRate = ((CPI / lag(CPI, 3, default = NA)) - 1) * 4 ,
CPICore3MRunRate = ((CPICore / lag(CPICore, 3, default = NA)) - 1) * 4,
PCE3MRunRate = ((PCE / lag(PCE, 3, default = NA))- 1) * 4,
PPI3MRunRate = ((PPI / lag(PPI, 3, default = NA)) - 1) * 4) %>%
dplyr::select(QuoteDate, CPI, CPICore, PCE, PPI,
CPI3MRunRate, CPICore3MRunRate, PCE3MRunRate, PPI3MRunRate)
# Pivot to Long format
PresentationDF = PresentationDF %>%
pivot_longer(-QuoteDate, names_to = "MetricName", values_to = "MetricValue") %>%
filter(complete.cases(.))
# Function to Get Change
MetricChange = function(MetricName, ChangeMonths) {
PresentationMetric = MetricName
CurrentMonth = max(PresentationDF$QuoteDate)
StartMonth = CurrentMonth - months(ChangeMonths)
StartMetricValue = filter(PresentationDF, MetricName == PresentationMetric,
QuoteDate == StartMonth)$MetricValue
EndingMetricValue = filter(PresentationDF, MetricName == PresentationMetric,
QuoteDate == CurrentMonth)$MetricValue
MetricValueChange = ((EndingMetricValue / StartMetricValue) - 1) / (ChangeMonths / 12)
return(MetricValueChange)
}
# Function to Plot
EconPlot = function(MetricName, StartLag, LineColor) {
PresentationMetric = MetricName
CurrentMonth = max(filter(PresentationDF, MetricName == PresentationMetric)$QuoteDate)
StartDate = CurrentMonth - months(StartLag)
DateRange = seq.Date(StartDate, CurrentMonth, by = "month")
PriorYear = CurrentMonth - months(12)
PriorYear2 = CurrentMonth - months(24)
PriorYear3 = CurrentMonth - months(36)
EndingMetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == CurrentMonth)$MetricValue
PriorYearMetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear)$MetricValue
PriorYear2MetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear2)$MetricValue
PriorYear3MetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear3)$MetricValue
MetricValue12MonthChange = (EndingMetricValue / PriorYearMetricValue) - 1
MetricValue24MonthChange = ((EndingMetricValue / PriorYear2MetricValue) - 1) / 2
MetricValue36MonthChange = ((EndingMetricValue / PriorYear3MetricValue) - 1) / 3
LineAdjPriorYear2 = EndingMetricValue +
(EndingMetricValue -
min(filter(PresentationDF, QuoteDate >= StartDate)$MetricValue)) * .01
LineAdjPriorYear2 = if_else(LineAdjPriorYear2 < EndingMetricValue + 1, EndingMetricValue + 1,
LineAdjPriorYear2)
LineAdjPriorYear3 = EndingMetricValue +
(EndingMetricValue -
min(filter(PresentationDF, QuoteDate >= StartDate)$MetricValue)) * .02
LineAdjPriorYear3 = if_else(LineAdjPriorYear3 < EndingMetricValue + 2, EndingMetricValue + 2,
LineAdjPriorYear3)
if (str_detect(.Platform$OS.type, "win")) {
# Windows uses "size" and linux uses "linewidth"
Plot1 = PresentationDF %>%
filter(QuoteDate >= StartDate) %>%
filter(MetricName == PresentationMetric) %>%
ggplot(aes(QuoteDate, MetricValue)) +
geom_line(linewidth = 2, color = LineColor, na.rm = TRUE) +
theme_classic() +
theme(text=element_text(size=16),
plot.caption=element_text(size=12),
axis.text=element_text(size=10),
axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_x_date(labels = date_format("%Y-%b"),
breaks = DateRange) +
labs(title = paste0("Annualized Changes in ", PresentationMetric),
color = "",
caption = paste0("Source : St. Louis Federal Reserve - FRED. As of ", Sys.Date()),
y = "",
x = "")
Plot1 +
# 12 Month Lines and Label
annotate("segment", size = 1,
x = PriorYear, xend = CurrentMonth ,
y = EndingMetricValue,
yend = EndingMetricValue,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", size = 2,
x = PriorYear, xend = PriorYear,
y = EndingMetricValue, yend = PriorYearMetricValue) +
annotate("label",
label = paste0("12Mo ", percent(MetricValue12MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear)),
y = EndingMetricValue) +
# 24 Month Lines and Label
annotate("segment", size = 1,
x = PriorYear2, xend = CurrentMonth ,
y = LineAdjPriorYear2,
yend = LineAdjPriorYear2,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", size = 2,
x = PriorYear2, xend = PriorYear2,
y = LineAdjPriorYear2, yend = PriorYear2MetricValue) +
annotate("label",
label = paste0("24Mo ", percent(MetricValue24MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear2)),
y = LineAdjPriorYear2) +
# 36 Month Lines and Label
annotate("segment", size = 1,
x = PriorYear3, xend = CurrentMonth ,
y = LineAdjPriorYear3,
yend = LineAdjPriorYear3,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", size = 2,
x = PriorYear3, xend = PriorYear3,
y = LineAdjPriorYear3, yend = PriorYear3MetricValue) +
annotate("label",
label = paste0("36Mo ", percent(MetricValue36MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear3)),
y = LineAdjPriorYear3)
} else {
# Windows uses "size" and linux uses "linewidth"
Plot1 = PresentationDF %>%
filter(QuoteDate >= StartDate) %>%
filter(MetricName == PresentationMetric) %>%
ggplot(aes(QuoteDate, MetricValue)) +
geom_line(linewidth = 2, color = LineColor, na.rm = TRUE) +
theme_classic() +
theme(text=element_text(size=16),
plot.caption=element_text(size=12),
axis.text=element_text(size=10),
axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_x_date(labels = date_format("%Y-%b"),
breaks = DateRange) +
labs(title = paste0("Annualized Changes in ", PresentationMetric),
color = "",
caption = paste0("Source : St. Louis Federal Reserve - FRED. As of ", Sys.Date()),
y = "",
x = "")
Plot1 +
# 12 Month Lines and Label
annotate("segment", linewidth = 1,
x = PriorYear, xend = CurrentMonth ,
y = EndingMetricValue,
yend = EndingMetricValue,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", linetype = 2,
x = PriorYear, xend = PriorYear,
y = EndingMetricValue, yend = PriorYearMetricValue) +
annotate("label",
label = paste0("12Mo ", percent(MetricValue12MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear)),
y = EndingMetricValue) +
# 24 Month Lines and Label
annotate("segment", linewidth = 1,
x = PriorYear2, xend = CurrentMonth ,
y = LineAdjPriorYear2,
yend = LineAdjPriorYear2,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", linetype = 2,
x = PriorYear2, xend = PriorYear2,
y = LineAdjPriorYear2, yend = PriorYear2MetricValue) +
annotate("label",
label = paste0("24Mo ", percent(MetricValue24MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear2)),
y = LineAdjPriorYear2) +
# 36 Month Lines and Label
annotate("segment", linewidth = 1,
x = PriorYear3, xend = CurrentMonth ,
y = LineAdjPriorYear3,
yend = LineAdjPriorYear3,
arrow = arrow(ends = "both", angle = 90, length = unit(.2,"cm"))) +
annotate("segment", linetype = 2,
x = PriorYear3, xend = PriorYear3,
y = LineAdjPriorYear3, yend = PriorYear3MetricValue) +
annotate("label",
label = paste0("36Mo ", percent(MetricValue36MonthChange, accuracy = .1)),
x = mean(c(CurrentMonth, PriorYear3)),
y = LineAdjPriorYear3)
}
}
# 2nd Function to Plot a 3 Month Running Rate Change * 4 to Annualize
EconPlot_RunRate = function(MetricName, StartLag, LineColor) {
PresentationMetric = MetricName
DisplayMetric = str_sub(MetricName, 1, str_locate(MetricName, "3M")[1]-1)
CurrentMonth = max(filter(PresentationDF, MetricName == PresentationMetric)$QuoteDate)
StartDate = CurrentMonth - months(StartLag)
DateRange = seq.Date(StartDate, CurrentMonth, by = "month")
PriorYear = CurrentMonth - months(12)
PriorYear2 = CurrentMonth - months(24)
PriorYear3 = CurrentMonth - months(36)
EndingMetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == CurrentMonth)$MetricValue
PriorYearMetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear)$MetricValue
PriorYear2MetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear2)$MetricValue
PriorYear3MetricValue = filter(PresentationDF, MetricName == PresentationMetric, QuoteDate == PriorYear3)$MetricValue
MetricValue12MonthChange = (EndingMetricValue / PriorYearMetricValue) - 1
MetricValue24MonthChange = ((EndingMetricValue / PriorYear2MetricValue) - 1) / 2
MetricValue36MonthChange = ((EndingMetricValue / PriorYear3MetricValue) - 1) / 3
LineAdjPriorYear2 = EndingMetricValue +
(EndingMetricValue -
min(filter(PresentationDF, QuoteDate >= StartDate)$MetricValue)) * .01
LineAdjPriorYear2 = if_else(LineAdjPriorYear2 < EndingMetricValue + 1, EndingMetricValue + 1,
LineAdjPriorYear2)
LineAdjPriorYear3 = EndingMetricValue +
(EndingMetricValue -
min(filter(PresentationDF, QuoteDate >= StartDate)$MetricValue)) * .02
LineAdjPriorYear3 = if_else(LineAdjPriorYear3 < EndingMetricValue + 2, EndingMetricValue + 2,
LineAdjPriorYear3)
if (str_detect(.Platform$OS.type, "win")) {
# Windows uses "size" and linux uses "linewidth"
Plot1 = PresentationDF %>%
filter(QuoteDate >= StartDate) %>%
filter(MetricName == PresentationMetric) %>%
ggplot(aes(QuoteDate, MetricValue)) +
geom_line(linewidth = 2, color = LineColor, na.rm = TRUE) +
theme_classic() +
theme(text=element_text(size=16),
plot.caption=element_text(size=12),
axis.text=element_text(size=10),
axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_x_date(labels = date_format("%Y-%b"),
breaks = DateRange) +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
labs(title = paste0("Rolling 3 Month Rate Change in ", DisplayMetric, " Annualized"),
color = "",
caption = paste0("Source : St. Louis Federal Reserve - FRED. As of ", Sys.Date()),
y = "",
x = "")
Plot1 +
# Ending Value Label
annotate("label",
label = paste0(percent(EndingMetricValue, accuracy = .1)),
x = CurrentMonth,
y = EndingMetricValue)
} else {
# Windows uses "size" and linux uses "linewidth"
Plot1 = PresentationDF %>%
filter(QuoteDate >= StartDate) %>%
filter(MetricName == PresentationMetric) %>%
ggplot(aes(QuoteDate, MetricValue)) +
geom_line(linewidth = 2, color = LineColor, na.rm = TRUE) +
theme_classic() +
theme(text=element_text(size=16),
plot.caption=element_text(size=12),
axis.text=element_text(size=10),
axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_x_date(labels = date_format("%Y-%b"),
breaks = DateRange) +
scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
labs(title = paste0("Rolling 3 Month Rate Change in ", DisplayMetric, " Annualized"),
color = "",
caption = paste0("Source : St. Louis Federal Reserve - FRED. As of ", Sys.Date()),
y = "",
x = "")
Plot1 +
# Ending Value Label
annotate("label",
label = paste0(percent(EndingMetricValue, accuracy = .1)),
x = CurrentMonth,
y = EndingMetricValue)
}
}The Federal Reserve is currently on record as saying that these inflation signals are mostly transitory. To test that statement, I’m going to set this analysis to refresh automatically so we can see the movement over the next year or more (I’m initially writing this in July of 2021).
Accordingly while the numbers cited in my comments with each chart will remain accurate via the automatic update, the intended contrast may not stay consistent.
The most common measure of inflation is the Consumer Price Index. This index tracks the weighted average cost of a basket of common goods and services purchased by US households. More information can be found at Investopedia’s CPI Page
EconPlot("CPI", 48, "dark red")
Here we can see that the shorter term 1 year inflation of 2.7% is significantly different than the longer term annualized 2 year rate of 2.8% or 3 year rate of 3.1%.
It’s also useful to look at shorter term trends as these 12 to 36 month trends take awhile to register near term impacts due to changes in government policy or other macroeconomic factors. Here we will look at the rolling change from 3 months ago annualized (multiplied by 4). There is no seasonal adjustment.
EconPlot_RunRate("CPI3MRunRate", 48, "dark red")
Here you can see the more drastic short term changes.
Next we will look at CPI Core Index which is similar to the CPI but it excludes energy and food which tend to be more volatile. Because that volatility is excluded, the CPI Core is favored as a better indicator of inflation in the short term. Investopedia’s Core CPI Page
EconPlot("CPICore", 48, "blue")
Here we can see the contrast in the 1yr change of the CPI Core at 2.6% versus the CPI at 2.7%.
Now the short term 3 month changes.
EconPlot_RunRate("CPICore3MRunRate", 48, "blue")
For an alternative to the CPI measures, the Federal Reserve prefers to gauge inflation using Personal Consumption Expenditures. The PCE looks at expenditures versus prices for both goods and services and therefore is self adjusting in terms of matching how consumers actually distribute their spending. Investopedia’s PCE Page
EconPlot("PCE", 48, "#ff0099")
Here we can see the contrast in the 1yr change of the PCE at versus the CPI at 2.7%.
Now the short term 3 month changes.
EconPlot_RunRate("PCE3MRunRate", 48, "#ff0099")
Finally we will look the Producer Price Index. The PPI looks at the changes in prices charged by producers, so it is focused on physical goods. Investopedia’s PPI Page
EconPlot("PPI", 48, "dark green")
The changes in the PPI are often more drastic than the CPI as producers are directly subject to labor and raw material costs while consumers have wholesalers and retailers to act as a buffer when price changes are temporary. The PPI 1yr change of versus the CPI at 2.7% may demonstrate this.
Now the short term 3 month changes.
EconPlot_RunRate("PPI3MRunRate", 48, "dark green")
In closing, don’t be don’t be transitory but instead,
be permanently savvy.