US 3-Month Treasury Bill Secondary Market Rate (FRED)
- Rows
- 18,885
- Columns
- 2
Daily 3-month T-bill rate from 1954 to present. A key short-term risk-free rate benchmark used as a baseline in financial time series, asset pricing, and portfolio research.
AI analysis
Dataset Analysis: US 3-Month Treasury Bill Secondary Market Rate (FRED)
1. Dataset Overview & Research Value This dataset captures the daily secondary market rate for the US 3-Month Treasury Bill (T-bill), spanning from 1954 to the present — a remarkable ~70-year window sourced directly from the Federal Reserve Bank of St. Louis (FRED) API at fred.stlouisfed.org. The FRED provenance is significant: it signals institutional reliability, regular updates, and alignment with official monetary policy data. With 18,885 daily observations, this series is one of the most foundational benchmarks in quantitative finance, widely used as a proxy for the risk-free rate in asset pricing models (e.g., CAPM, Sharpe ratio calculations), yield curve construction, and macroeconomic regime analysis. Its correlation potential is exceptionally broad — virtually any financial, economic, or credit-related time series can be meaningfully paired with T-bill rates.
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2. Data Quality Observations Overall quality is strong for a dataset of this age and scope, but there are notable concerns. The 796 null values in DTB3 (~4.2% of rows) are the primary quality issue and are entirely concentrated in a single column, suggesting systematic gaps rather than random missingness — likely attributable to federal holidays, weekends included in the date spine, or brief market closures rather than data collection failures. No duplicate rows are flagged (though the Phase D recompute is still pending confirmation). The Date column is clean with zero nulls and 18,885 distinct values, confirming a fully unique daily date index — ideal for time-series joining. The negative minimum value of -0.05 is technically valid but worth flagging: near-zero or sub-zero T-bill rates occurred during post-2008 quantitative easing and COVID-era monetary conditions and should be treated as economically meaningful, not errors.
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3. Key Column Distributions The DTB3 column tells a rich macroeconomic story through its statistics. The mean of 4.19% and median of 4.12% are remarkably close, suggesting a roughly symmetric long-run distribution, yet the positive skew of 0.876 indicates a right tail — driven by the extreme rate environment of the late 1970s/early 1980s, where the max reached 17.14% under Volcker-era Fed tightening. The interquartile range (Q1=1.87%, Q3=5.66%) reflects that rates spent considerable time in low-to-moderate territory, particularly post-2008. The standard deviation of 3.05% is high relative to the current rate environment, underscoring the regime-dependent volatility in this series. The 533 statistical outliers are historically meaningful rather than erroneous — they cluster around known monetary policy extremes and should be retained for full-cycle analysis rather than winsorized prematurely.
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4. Recommended Join Key Columns The Date column is the clear and sole join key — it is fully distinct (18,885 unique values), null-free, and typed as Date, making it immediately usable for time-series merges. When joining to datasets with different frequencies (monthly CPI, quarterly GDP), date resampling or aggregation (e.g., monthly mean of DTB3) will be necessary. For cross-asset joins, aligning on trading-day calendars is advisable to avoid spurious nulls from the ~4.2% holiday/weekend gaps already present.
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5. Suggested Correlation Datasets The following dataset types pair naturally with this series for high-value correlation discovery:
| Dataset | Rationale | |---|---| | Federal Funds Rate (FEDFUNDS, FRED) | Near-perfect co-movement expected; reveals monetary policy transmission lags | | 10-Year Treasury Yield (DGS10, FRED) | Enables yield curve slope analysis — a classic recession predictor | | S&P 500 / Equity Returns | Tests the risk premium relationship and safe-haven rotation dynamics | | CPI / Inflation Data (BLS) | Examines real vs. nominal rate divergence and Taylor Rule dynamics | | Corporate Bond Spreads (ICE BofA) | Credit risk premium widens as T-bill rates rise — strong inverse relationship | | USD Exchange Rates | Interest rate differentials drive FX carry trade correlations |
Pairing this dataset with the 10-Year yield and CPI in particular would unlock a powerful macro trifecta for studying interest rate regimes, inflationary cycles, and their downstream effects on asset classes.
Columns
- Date (date)
- DTB3 (decimal)
