10-Year US Treasury Constant Maturity Rate (FRED)

Rows: 16,799
Columns: 2

Daily 10-year US Treasury constant maturity yield from 1962 to present. A key government interest rate benchmark used across financial and economic research.

AI analysis

Dataset Analysis: 10-Year US Treasury Constant Maturity Rate (FRED)

1. Dataset Overview & Research Value This dataset captures the daily 10-year US Treasury constant maturity yield spanning from 1962 to the present — over six decades of one of the most consequential interest rate benchmarks in global finance. Sourced directly from the Federal Reserve Bank of St. Louis (FRED) via its public API at fred.stlouisfed.org, this data carries strong institutional credibility and is actively maintained, meaning it reflects near-real-time market conditions. The 10-year Treasury yield serves as a foundational reference rate for mortgage pricing, corporate bond spreads, equity valuation models (discount rates), and monetary policy signaling. For correlation studies, it is exceptionally versatile — virtually any macroeconomic, financial, or even behavioral dataset can be meaningfully tested against it, making this one of the highest-value single-column time series available in public finance data.

2. Data Quality Observations The dataset is structurally clean with 16,799 rows, zero null dates, and no duplicate rows flagged, which is expected for a well-maintained daily time series. The primary quality concern is the 716 null values in DGS10 (4.3% of rows), which is the sole data-bearing column. This is not alarming in context — FRED deliberately omits values for weekends, federal holidays, and market closures rather than forward-filling, so these nulls are structurally meaningful rather than indicative of data corruption. Analysts should confirm this assumption by verifying that nulls cluster on non-business days before imputing or dropping them. There are no type mismatches reported, and the date column shows 16,799 distinct values matching total row count exactly, confirming no duplicate dates exist.

3. Distribution & Statistical Highlights The DGS10 yield column tells a rich story across its 60+ year history. The mean of 5.81% sits notably above the median of 5.42%, and the positive skew of 0.749 reflects the heavy influence of the historically extreme rate environment of the late 1970s and early 1980s, when yields peaked at 15.84% (the column maximum). The interquartile range spans 3.89% (Q1) to 7.54% (Q3), a wide spread reflecting multiple full rate cycles. The minimum of 0.52% captures the near-zero rate environment of 2020–2021. The 447 statistical outliers (roughly 2.7% of non-null observations) likely cluster in two eras: the 1980s high-rate period on the upper end, and the post-2008/COVID low-rate period on the lower end — both economically meaningful extremes rather than data errors. The standard deviation of 2.93 reinforces just how volatile this rate has been across its full history.

4. Recommended Join Key The Date column is the clear and only join key, with 16,799 distinct values and zero nulls, making it a reliable anchor for time-series merges. When joining to other datasets, analysts should account for business-day-only coverage — weekend and holiday dates will be absent from this series. A left join from a calendar or macro dataset onto this one is preferable to avoid spurious gaps. For datasets with monthly or quarterly frequency (e.g., GDP, CPI), the Date column can be aggregated to period-end or period-average yields before joining.

5. Recommended Companion Datasets for Correlation Analysis Several dataset categories would pair powerfully with this series. Equity market indices (S&P 500, Nasdaq) are natural candidates, as rising yields historically compress price-to-earnings multiples — a negative correlation worth quantifying across rate regimes. Inflation data (CPI, PCE) from FRED would test whether the yield curve anticipates or lags inflation, a core question in monetary economics. 30-year fixed mortgage rates (also available from FRED: MORTGAGE30US) would likely show near-perfect correlation given their direct pricing dependency on the 10-year. Corporate bond spreads (e.g., ICE BofA High Yield Index) would reveal risk-appetite dynamics. Finally, housing starts or home sales data could surface a lagged macroeconomic transmission effect, where rate changes ripple into real economic activity with a 3–6 month delay — a compelling causal hypothesis to test.

Columns

Date (date)
DGS10 (decimal)