Mixed-Frequency Data for Macroeconomic and Financial Forecasting

This dataset, associated with a Federal Reserve Bank of St. Louis Review article, addresses the challenge of forecasting with variables sampled at different frequencies. It likely contains macroeconomic indicators like employment and GDP alongside higher-frequency financial variables such as interest rates and asset prices. The data supports methodological comparisons for handling mixed-frequency data, including averaging and mixed data sampling (MIDAS) techniques.

Use Cases

• Comparing forecasting performance of averaging versus explicit mixed-frequency modeling based on described methods.
• Evaluating intra-period forecast construction using mixed data sampling (MIDAS) techniques.
• Benchmarking econometric models that handle monthly/quarterly macroeconomic data alongside daily financial data.

Strengths

• Addresses a core methodological challenge in econometrics and forecasting.
• Associated with a formal publication from the Federal Reserve Bank of St. Louis, suggesting a research-grade context.

Limitations

• Critical metadata is missing: no column names, row count, or specific time range are provided in the sources.
• The license is listed as 'closed', restricting access and reuse.
• Sources show conflicting tags (e.g., 'Environmental science'), indicating potential metadata noise.

Provenance

Source

Federal Reserve Bank of St. Louis Review, authored by Michelle T. Armesto.

Collection Method

Likely compiled or referenced for a methodological survey on mixed-frequency forecasting.