Topic 4 – US Macroeconomic Indicators (Kaggle)

Dataset

• Source: U.S. Economic Time Series – Kaggle
• Link: https://www.kaggle.com/datasets/utkarshx27/us-economic-time-series

Download Instructions

1. Open the dataset link above.
2. Click "Download".
3. Extract to data/.
4. Use the main CSV, e.g. USEconomicData.csv.

Data Loading

import pandas as pd
df = pd.read_csv("data/USEconomicData.csv")  # adjust filename
df["DATE"] = pd.to_datetime(df["DATE"])
df = df.set_index("DATE").sort_index()

Implementation Steps

1. Data Exploration

• Load dataset and explore available indicators
• Select key indicators to analyze (e.g., unemployment rate, inflation, GDP, interest rates)
• Inspect data frequency (monthly, quarterly, annual)
• Check for missing values and data quality issues

2. Exploratory Data Analysis (EDA)

• Plot each selected indicator over time
• Identify trends, cycles, and structural breaks
• Calculate correlations between indicators
• Perform time series decomposition for each series
• Analyze ACF/PACF for each indicator

3. Stationarity Analysis

• Test each indicator for stationarity (ADF test)
• Apply appropriate transformations (differencing, log transformation)
• Handle non-stationarity appropriately
• Document transformation decisions

4. Model Building

• Univariate Models:
• ARIMA/SARIMA for each indicator individually
• Model selection using AIC/BIC
• Multivariate Models (optional):
• VAR (Vector Autoregression) models
• Analyze relationships between indicators
• Granger causality tests

5. Model Evaluation

• Split data temporally (keep recent data for testing)
• Generate forecasts for each indicator
• Calculate forecast accuracy metrics (MAE, RMSE, MAPE)
• Compare univariate vs multivariate approaches
• Visualize forecasts with actual values

6. Economic Interpretation

• Interpret model results in economic context
• Discuss relationships between indicators
• Analyze forecast implications
• Compare with economic theory

Expected Deliverables

1. EDA Report:

• Time series plots for each indicator
• Correlation analysis
• Decomposition plots
• Stationarity test results

1. Model Results:

• Model parameters for each indicator
• Performance metrics table
• Forecast plots
• Economic interpretation

1. Code:

• Complete Python notebook
• Functions for indicator analysis
• Visualization utilities

Tips

• Macroeconomic indicators often have strong trends and cycles
• Consider external factors (recessions, policy changes) in analysis
• VAR models can capture relationships between indicators
• Use appropriate frequency (monthly vs quarterly) based on data availability
• Economic indicators may have different units - normalize for comparison
• Consider leading/lagging relationships between indicators
• Document economic context and policy implications