How to Use EDA to Investigate the Impact of Interest Rates on Consumer Spending

Understanding the relationship between interest rates and consumer spending is crucial for economists, financial analysts, and businesses. Exploratory Data Analysis (EDA) provides a systematic approach to uncover patterns, anomalies, and relationships within datasets, helping analysts form insights and hypotheses. This article outlines a practical guide on how to use EDA to investigate the impact of interest rates on consumer spending, including steps from data sourcing to visualization and interpretation.

Define the Objective

The primary goal is to explore how fluctuations in interest rates influence consumer spending behavior. Interest rates directly affect borrowing costs, which in turn can stimulate or suppress spending across categories such as housing, automobiles, and retail.

Key Questions to Address:

• Is there a correlation between interest rate changes and consumer spending patterns?

• How does consumer spending respond across different income levels?

• Are specific spending categories more sensitive to interest rate shifts?

Step 1: Data Collection

Gather data from reputable sources that reflect both interest rates and consumer spending metrics over a significant time period. Recommended datasets include:

Interest Rates:

• Federal Reserve Economic Data (FRED): Offers historical interest rate data (e.g., federal funds rate, mortgage rates).

• World Bank or IMF: For international comparisons.

Consumer Spending:

• Bureau of Economic Analysis (BEA): Personal Consumption Expenditures (PCE).

• US Census Bureau: Retail trade and service sector reports.

• Credit card or bank transaction datasets (if available) for micro-level analysis.

Auxiliary Variables:

Include variables such as inflation, unemployment rate, consumer confidence index, and GDP growth to control for external influences.

Step 2: Data Preprocessing

Before analysis, ensure the data is clean, well-structured, and properly aligned in terms of time intervals.

Tasks Include:

• Handling Missing Values: Use interpolation for time series data or imputation techniques.

• Standardizing Timeframes: Align datasets to a common frequency (monthly, quarterly).

• Normalization: Scale data when comparing variables with different units (e.g., z-score normalization).

• Merging Datasets: Integrate interest rate data with consumer spending using date/time as a common key.

Step 3: Initial Data Exploration

Use descriptive statistics to understand the data’s distribution and characteristics.

Techniques:

• Summary Statistics: Mean, median, variance, and standard deviation.

• Distribution Plots: Histograms and density plots to assess skewness and kurtosis.

• Time Series Visualization: Line plots showing trends in interest rates and consumer spending over time.

Look for patterns such as cyclical behavior in spending relative to interest rate hikes or drops.

Step 4: Correlation Analysis

Examine the relationship between interest rates and consumer spending.

Methods:

• Pearson/Spearman Correlation Coefficients: Quantify the strength and direction of association.

• Heatmaps: Visualize the correlation matrix between variables.

• Scatter Plots: Show the direct relationship between changes in interest rates and spending levels.

Look for lag effects—spending might not immediately respond to interest rate changes.

Step 5: Time Series Decomposition

Break down both interest rate and spending data into trend, seasonality, and residual components.

Tools:

• Moving Averages: Smooth out short-term fluctuations.

• Seasonal Decomposition of Time Series (STL): Identify seasonal effects that may obscure the interest rate impact.

• Rolling Correlation: Analyze how the relationship between interest rates and spending evolves over time.

This step helps separate genuine long-term impacts from temporary anomalies or seasonal behavior.

Step 6: Segmentation Analysis

Explore how the impact varies across different segments.

Dimensions to Segment By:

• Income Levels: High vs. low-income consumers.

• Geographic Regions: States, countries, or urban vs. rural.

• Spending Categories: Durable goods, non-durables, services.

Use box plots, violin plots, or facet grids to visualize differences across these groups. This reveals whether certain populations are more interest rate-sensitive.

Step 7: Feature Engineering

Create derived features that could highlight hidden relationships.

Examples:

• Rate Change Indicators: Binary variables indicating rate hikes or drops.

• Rate Slope: The rate of change over a rolling window.

• Spending Volatility: Standard deviation of spending over time.

These features enrich the dataset and allow for deeper multivariate analysis.

Step 8: Regression Modeling for Causal Inference

While EDA is primarily exploratory, simple regression models can help quantify relationships.

Approaches:

• Linear Regression: Measure the direct effect of interest rate changes on consumer spending.

• Lagged Regression: Account for delayed effects.

• Multivariate Models: Control for confounders like inflation, unemployment, and income.

Plotting residuals and checking for heteroskedasticity helps validate model assumptions.

Step 9: Visualization and Storytelling

Use data visualization to communicate insights effectively.

Visualization Types:

• Dual-Axis Time Series: Show interest rate and spending on one chart with two y-axes.

• Bubble Charts: Represent three variables (e.g., spending, rate, income).

• Dashboards: Combine multiple visualizations for interactive exploration.

Ensure visualizations are clear, labeled, and accessible to both technical and non-technical audiences.

Step 10: Key Insights and Hypothesis Formulation

Summarize your findings and develop hypotheses for further testing or predictive modeling.

Potential Insights:

• Consumer spending on durable goods drops significantly 3–6 months after an interest rate hike.

• Low-income households show a more pronounced spending contraction.

• Interest-sensitive categories like housing and automotive respond more strongly than food or healthcare.

Use these insights to guide further statistical modeling, policy analysis, or business strategy.

Conclusion

EDA provides a powerful framework for uncovering how interest rates affect consumer spending. By systematically collecting, cleaning, visualizing, and analyzing the data, one can identify meaningful patterns and relationships. While EDA itself doesn’t confirm causality, it lays the groundwork for more advanced analyses, such as econometric modeling or machine learning. Understanding these dynamics is essential for policymakers, economists, and businesses aiming to navigate the ever-changing economic landscape.