Federal Funds Effective Rate Daily (FRED)
- Rows
- 26,402
- Columns
- 2
Daily federal funds effective interest rate from 1954 to present. The key U.S. monetary policy rate set by the Federal Reserve, critical for financial time series modeling.
AI analysis
Federal Funds Effective Rate (DFF) — Dataset Analysis
1. Dataset Overview & Value for Correlation Studies This dataset captures the daily Federal Funds Effective Rate (DFF) sourced directly from the Federal Reserve Bank of St. Louis (FRED) via their public API at fred.stlouisfed.org. The FRED platform is maintained by one of the most authoritative economic data publishers in the world, and the source URL pattern (fredgraph.csv?id=DFF) confirms this is a live API endpoint, meaning the dataset can be refreshed to reflect the most current monetary policy decisions. Spanning from 1954 to present across 26,262 daily observations, this represents roughly 70 years of U.S. monetary policy history — making it exceptionally valuable for long-horizon time series modeling, regime analysis, and macro-financial correlation studies. The federal funds rate is the foundational benchmark from which nearly all other interest rates and credit conditions are derived, giving this dataset outsized influence as an independent or control variable in financial research.
2. Data Quality Observations Data quality here is remarkably clean for a financial time series. There are zero null cells across both columns, which is notable given the dataset spans decades and includes weekends/holidays where some financial series go silent. The Date column shows 26,262 distinct values matching the total row count exactly, confirming no duplicate dates — each row is a unique daily observation. The DFF column yields 1,560 distinct values across 26,262 rows, which is entirely expected given that the Fed rate holds steady for extended periods between policy meetings. No type mismatches are flagged, and the duplicate row count (pending Phase D Parquet recomputation) appears structurally clean given the date uniqueness. The 840 flagged outliers deserve attention — likely concentrated in the early 1980s Volcker-era rate spikes — but these are genuine economic events, not data errors, and should be retained rather than suppressed.
3. Key Column Distributions The DFF column tells a rich distributional story. The mean of 4.60% sits above the median of 4.31%, and the right skew of 1.11 confirms a long tail toward high rates — driven by the 1979–1982 tightening cycle where rates peaked at the recorded maximum of 22.36%. The minimum of 0.04% reflects the near-zero rate environment maintained post-2008 financial crisis and again during COVID-19 (2020–2022). The interquartile range of Q1=1.91% to Q3=6.13% captures the bulk of modern rate history, with a standard deviation of 3.55 indicating substantial volatility across regimes. This bimodal-ish character — long periods near zero, long periods at 5–6%+, and a historic spike above 20% — means standard linear correlation assumptions may need regime-conditioning or log transformation before modeling.
4. Recommended Join Key Columns The Date column is the natural and only join key here, and its quality is ideal: zero nulls, 26,262 distinct values with no duplicates, and typed as Date. For cross-dataset joins, note that DFF is reported on calendar days including weekends, whereas many financial datasets (equities, bond yields) report on business days only — requiring a date normalization or forward-fill strategy on join. A business-day calendar filter or a weekly/monthly aggregation of DFF (mean or end-of-period) will be necessary when joining to market data. For macro datasets (CPI, GDP), a month-end or quarterly aggregation of DFF should be pre-computed as a secondary join key.
5. Recommended Pairing Datasets for Correlation Analysis Several dataset categories would unlock high-value correlations with DFF:
- Treasury Yield Curve (e.g., FRED series DGS2, DGS10, DGS30) — to model yield curve shape, spread compression/inversion, and term premium dynamics relative to the policy rate - Inflation measures (CPI, PCE from FRED) — the Fed explicitly targets 2% inflation; DFF vs. CPI correlations reveal Taylor Rule dynamics and policy lag effects - S&P 500 or equity indices (Yahoo Finance, CRSP) — rate levels and rate-change regimes are well-known equity valuation drivers; a rate × market return correlation study across regimes would be analytically powerful - Mortgage rates (FRED MORTGAGE30US) and housing price indices (Case-Shiller, FHFA) — to trace monetary policy transmission into real estate - Corporate credit spreads (ICE BofA indices via FRED) — to study how fed funds rate cycles interact with credit risk appetite and default probabilities
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