Introduction

Economic indicators provide insights into the current state and future trends of the economy. In this analysis, we construct a diffusion index using three U.S. economic variables: Industrial Production Index (INDPRO), Personal Consumption Expenditures (PCE), and Unemployment Rate (UNRATE). These indicators reflect production activity, consumer spending, and labor market health.

This analysis is part of the coursework for Pompea College of Business, University of New Haven. My name is Pratyusha Mandadi, and I hope this report provides useful insights.

Data Collection

The data for these indicators has been sourced from FRED.

getSymbols(c("INDPRO", "PCE", "UNRATE"), 
           src = "FRED", 
           return.class = 'xts',
           from = "2010-01-01",
           to = Sys.Date(),
           auto.assign = TRUE)
## [1] "INDPRO" "PCE"    "UNRATE"
# Combine data into one dataset
us_data <- merge(INDPRO, PCE, UNRATE)
colnames(us_data) <- c("Industrial_Production", "PCE", "Unemployment_Rate")

Data Standardization

To construct the diffusion index, the indicators need to be standardized.

us_data_scaled <- us_data %>%
  na.omit() %>%
  scale() %>%
  as.xts()

Constructing the Diffusion Index

The diffusion index is the average of the standardized values of the selected indicators.

us_data_diffusion <- rowMeans(us_data_scaled, na.rm = TRUE)
us_data_diffusion <- xts(us_data_diffusion, order.by = index(us_data_scaled))

Plotting the Diffusion Index

The following plot visualizes the diffusion index over time.

Comparison with Chicago Fed National Activity Index

The CFNAI Diffusion Index provides a benchmark for comparison.

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)

combined_data <- merge(us_data_diffusion_aligned, CFNAI_aligned, join = "inner")
colnames(combined_data) <- c("Diffusion_Index", "Chicago_Fed_Index")

Correlation Analysis

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"

Plotting Both Indexes

Insights and Conclusion

The diffusion index constructed using Industrial Production, Personal Consumption Expenditures, and Unemployment Rate shows a strong correlation with the CFNAI Diffusion Index. This suggests that these indicators collectively capture a broad view of U.S. economic activity. Further analysis could explore divergences in the two indexes and their implications for policy and forecasting.

This analysis was conducted as part of the coursework for Pompea College of Business, University of New Haven. Thank you for reviewing this work.