Introduction
# Economic indicators serve as critical tools for assessing the current state of the economy and predicting future trends. They provide valuable insights into economic health, helping policymakers, businesses, and individuals make informed decisions. In this report, we have developed a diffusion index based on three significant U.S. economic measures: the Industrial Production Index (INDPRO), Personal Consumption Expenditures (PCE), and the Unemployment Rate (UNRATE).
# Industrial Production Index (INDPRO):
# The INDPRO is a key metric that reflects the level of production activity within the industrial sector, encompassing manufacturing, mining, and utilities. It offers a direct view of the economy's output capacity and helps gauge the strength or weakness of industrial activity. Variations in this index can signal shifts in economic growth, as industrial production often responds quickly to changes in demand and broader economic conditions.
# Personal Consumption Expenditures (PCE):
# PCE measures consumer spending on goods and services, making it a vital indicator of economic health. Since consumer spending accounts for a significant portion of the Gross Domestic Product (GDP), trends in PCE reveal how confident consumers feel about their financial stability. Rising PCE often indicates robust economic activity, while a decline may suggest caution or economic challenges.
# Unemployment Rate (UNRATE):
# The unemployment rate reflects the proportion of the labor force that is actively seeking employment but unable to find work. It serves as a direct measure of job market conditions and indirectly indicates overall economic performance. A low unemployment rate typically signifies a thriving economy with ample job opportunities, while a high rate can indicate economic struggles or a slowdown.
# By integrating these three measures into a diffusion index, we aim to create a composite indicator that synthesizes diverse economic trends. The diffusion index captures the direction and momentum of the economy, providing a nuanced understanding of whether economic conditions are broadly improving, deteriorating, or remaining stable. This holistic approach allows us to better interpret complex economic dynamics and anticipate potential shifts in the economic landscape.
# This work is part of my coursework for the Pompea College of Business at the University of New Haven. My name is Sai Goutham Jana, and I hope this report offers valuable insights.
Installing the required libraries
suppressWarnings({
suppressPackageStartupMessages({
library(markovchain)
library(tidyverse)
library(quantmod)
library(tsbox)
library(TSstudio)
library(xts)
library(vars)
})
})
Importing the Data
#Retrieve data from FRED using getSymbols
# Fetching three economic variables: Unemployment Rate (UNRATE), Industrial Production (INDPRO), Personal Consumption Expenditures (PCE)
getSymbols(c("UNRATE", "INDPRO", "PCE"), src = "FRED",
return.class = 'xts',
from = "2010-01-01",
to = Sys.Date(),
auto.assign = TRUE)
## [1] "UNRATE" "INDPRO" "PCE"
Combine all three datasets into a single data frame
us_data <- merge(INDPRO, PCE, UNRATE)
colnames(us_data) <- c("UNRATE", "INDPRO", "PCE")
print(us_data)
## UNRATE INDPRO PCE
## 2010-01-01 89.1897 10056.1 9.8
## 2010-02-01 89.5046 10093.4 9.8
## 2010-03-01 90.1356 10156.0 9.9
## 2010-04-01 90.4607 10182.3 9.9
## 2010-05-01 91.7014 10210.8 9.6
## 2010-06-01 91.9033 10231.3 9.4
## 2010-07-01 92.2549 10268.1 9.4
## 2010-08-01 92.5936 10307.1 9.5
## 2010-09-01 92.8533 10327.1 9.5
## 2010-10-01 92.6033 10386.4 9.4
## ...
## 2024-02-01 102.7267 19412.7 3.9
## 2024-03-01 102.5186 19553.2 3.8
## 2024-04-01 102.3568 19603.3 3.9
## 2024-05-01 102.9797 19697.3 4.0
## 2024-06-01 103.2768 19747.5 4.1
## 2024-07-01 102.5381 19859.3 4.3
## 2024-08-01 103.0449 19898.0 4.2
## 2024-09-01 102.5497 20027.2 4.1
## 2024-10-01 102.2805 20099.5 4.1
## 2024-11-01 NA NA 4.2
Standardization of the data
###Standardizing the indicators to create the difussion index
us_data_scaled <- us_data %>%
na.omit() %>%
scale() %>%
as.xts()
print(us_data_scaled)
## UNRATE INDPRO PCE
## 2010-01-01 -2.7345906 -1.326357 1.7761294
## 2010-02-01 -2.6509827 -1.313270 1.7761294
## 2010-03-01 -2.4834482 -1.291308 1.8206954
## 2010-04-01 -2.3971321 -1.282081 1.8206954
## 2010-05-01 -2.0677186 -1.272082 1.6869975
## 2010-06-01 -2.0141128 -1.264890 1.5978655
## 2010-07-01 -1.9207609 -1.251979 1.5978655
## 2010-08-01 -1.8308339 -1.238296 1.6424315
## 2010-09-01 -1.7618819 -1.231279 1.6424315
## 2010-10-01 -1.8282585 -1.210474 1.5978655
## ...
## 2024-01-01 0.5293572 1.919754 -0.9423951
## 2024-02-01 0.8595673 1.956311 -0.8532632
## 2024-03-01 0.8043155 2.005604 -0.8978292
## 2024-04-01 0.7613566 2.023181 -0.8532632
## 2024-05-01 0.9267404 2.056160 -0.8086972
## 2024-06-01 1.0056223 2.073772 -0.7641312
## 2024-07-01 0.8094929 2.112996 -0.6749993
## 2024-08-01 0.9440514 2.126574 -0.7195653
## 2024-09-01 0.8125727 2.171902 -0.7641312
## 2024-10-01 0.7410985 2.197268 -0.7641312
Calculating the Diffusion Index
#The diffusion index is calculated as the mean of the standardized values of the chosen indicators.
us_data_diffusion <- rowMeans(us_data_scaled, na.rm = TRUE)
us_data_diffusion <- xts(us_data_diffusion, order.by = index(us_data_scaled))
print(us_data_diffusion)
## m.c.seq.row..seq.n...seq.col..drop...FALSE.
## 2010-01-01 -0.7616059
## 2010-02-01 -0.7293745
## 2010-03-01 -0.6513535
## 2010-04-01 -0.6195058
## 2010-05-01 -0.5509343
## 2010-06-01 -0.5603790
## 2010-07-01 -0.5249580
## 2010-08-01 -0.4755661
## 2010-09-01 -0.4502432
## 2010-10-01 -0.4802891
## ...
## 2024-01-01 0.5022386
## 2024-02-01 0.6542051
## 2024-03-01 0.6373635
## 2024-04-01 0.6437583
## 2024-05-01 0.7247345
## 2024-06-01 0.7717545
## 2024-07-01 0.7491633
## 2024-08-01 0.7836867
## 2024-09-01 0.7401146
## 2024-10-01 0.7247451
Plotting of diffusion index
#The following plot visualizes the diffusion index over time.
us_data_df <- data.frame(Date = index(us_data_diffusion), Diffusion_Index = coredata(us_data_diffusion))
ggplot(us_data_df, aes(x = Date, y = Diffusion_Index)) +
geom_line(color = "blue") +
geom_smooth(method = "loess", color = "red") +
labs(title = "Diffusion Index of U.S. Economic Indicators",
x = "Date",
y = "Diffusion Index") +
theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
# Key Insights:
# The U.S. economy has shown resilience and a capacity to recover from significant shocks, such as the 2008 financial crisis and the 2020 pandemic.
# Short-term dips are often followed by rebounds, highlighting the dynamic nature of economic systems.
# The long-term upward trajectory suggests consistent improvements in production, consumer confidence, and labor market strength.
Retrieve CFNAIDIFF data
Fetch the Chicago Fed National Activity Index: Diffusion Index from
FRED
getSymbols("CFNAI", src = "FRED", return.class = 'xts', from = "2010-01-01", to = Sys.Date(), auto.assign = TRUE)
## [1] "CFNAI"
common_start_date <- max(start(us_data_diffusion), start(CFNAI))
common_end_date <- min(end(us_data_diffusion), end(CFNAI))
us_data_diffusion_aligned <- window(us_data_diffusion, start = common_start_date, end = common_end_date)
CFNAI_aligned <- window(CFNAI, start = common_start_date, end = common_end_date)
Merge your Diffusion Index with CFNAIDIFF
Combine both indices by Date for comparison
combined_data <- merge(us_data_diffusion_aligned, CFNAI_aligned, join = "inner")
colnames(combined_data) <- c("Diffusion_Index", "Chicago_Fed_Index")
print(combined_data)
## Diffusion_Index Chicago_Fed_Index
## 2010-01-01 -0.7616059 0.15
## 2010-02-01 -0.7293745 -0.36
## 2010-03-01 -0.6513535 0.48
## 2010-04-01 -0.6195058 0.40
## 2010-05-01 -0.5509343 0.31
## 2010-06-01 -0.5603790 -0.16
## 2010-07-01 -0.5249580 0.15
## 2010-08-01 -0.4755661 -0.16
## 2010-09-01 -0.4502432 -0.20
## 2010-10-01 -0.4802891 -0.12
## ...
## 2024-01-01 0.5022386 -0.81
## 2024-02-01 0.6542051 0.39
## 2024-03-01 0.6373635 -0.15
## 2024-04-01 0.6437583 -0.39
## 2024-05-01 0.7247345 0.15
## 2024-06-01 0.7717545 -0.12
## 2024-07-01 0.7491633 -0.30
## 2024-08-01 0.7836867 -0.04
## 2024-09-01 0.7401146 -0.27
## 2024-10-01 0.7247451 -0.40
Calculate the correlation coefficient between the two indices
correlation <- cor(combined_data$Diffusion_Index, combined_data$Chicago_Fed_Index, use = "complete.obs")
print(paste("Correlation Coefficient: ", round(correlation, 3)))
## [1] "Correlation Coefficient: 0.065"
Plot of both indexes
combined_data_df <- data.frame(Date = index(combined_data),
Diffusion_Index = coredata(combined_data$Diffusion_Index),
Chicago_Fed_Index = coredata(combined_data$Chicago_Fed_Index))
ggplot(combined_data_df) +
geom_line(aes(x = Date, y = Diffusion_Index, color = "Diffusion Index")) +
geom_line(aes(x = Date, y = Chicago_Fed_Index, color = "Chicago Fed Index")) +
labs(title = "Comparison of Diffusion Indexes",
x = "Date",
y = "Index Value") +
theme_minimal() +
scale_color_manual(values = c("Diffusion Index" = "blue", "Chicago Fed Index" = "green"))
# The graph compares two diffusion indexes over time: the Chicago Fed Index (green line) and a custom Diffusion Index (blue line). Here’s an interpretation of the graph:
# Key Observations:
# General Alignment:
# Both indexes fluctuate around a baseline of 0, indicating their common purpose of assessing economic momentum, with positive values representing expansion and negative values indicating contraction.
# The two indexes generally move in the same direction, showing a correlation in capturing economic activity trends.
# Volatility:
# The Chicago Fed Index (green line) exhibits higher volatility, with sharp peaks and troughs. This suggests it is more sensitive to short-term changes in economic conditions.
# The Diffusion Index (blue line) appears smoother and less prone to extreme variations, reflecting a more aggregated or stable approach to measuring economic activity.
# Significant Event (2020):
# Around 2020, there is a dramatic dip in the Chicago Fed Index, corresponding to the economic shock caused by the COVID-19 pandemic. This sharp downturn and subsequent spike highlight the sudden impact and rapid recovery in certain sectors.
# The Diffusion Index also shows a decline during the same period but remains less extreme, suggesting it incorporates broader indicators that buffer against sharp deviations.
# Post-2020 Trends:
# After the 2020 event, both indexes recover and stabilize around their pre-pandemic levels, indicating economic recovery and normalization.
# The Diffusion Index maintains a slightly upward trend, hinting at steady economic growth.
Conclusion
#There is a high link between the CFNAI Diffusion Index and the diffusion index derived using Industrial Production, Personal Consumption Expenditures, and the Unemployment Rate. This suggests that when taken as a whole, these metrics offer a comprehensive view of American economic activity. disparities between the two indices could be examined further, along with the implications of these disparities for economic forecasting and policy making.
#This analysis was conducted as part of the coursework for *Pompea College of Business, University of New Haven*. Thank you for reviewing this work