FRED – 5-Year Breakeven Inflation Rate
- Rows
- 6,104
- Columns
- 2
Daily 5-year inflation breakeven rate derived from TIPS from 2003 to present. Forward-looking inflation expectations time series.
AI analysis
FRED 5-Year Breakeven Inflation Rate — Dataset Analysis
1. What This Dataset Represents This dataset captures the 5-Year Breakeven Inflation Rate (T5YIE), a market-derived measure of inflation expectations computed as the spread between nominal 5-year Treasury yields and 5-year TIPS (Treasury Inflation-Protected Securities) yields. Published by the Federal Reserve Bank of St. Louis (FRED) and sourced directly via their public API at fred.stlouisfed.org, this is a high-credibility, institutionally maintained time series spanning from 2003 to the present — making it one of the most reliable proxies for forward-looking inflation sentiment available. Its daily frequency and long history make it exceptionally well-suited for macroeconomic correlation studies, monetary policy research, and financial market regime analysis.
2. Data Quality Observations The dataset is compact and structurally clean, with exactly 2 columns and 6,104 rows, and zero null values in the Date column, confirming a complete, uninterrupted date index. The primary quality concern is the 251 null values in T5YIE (4.1% of rows), which almost certainly correspond to non-trading days (weekends, federal holidays) when TIPS markets are closed — a structural, expected pattern rather than a data integrity failure, but one that requires handling (forward-fill, interpolation, or filtering) before any time-series modeling or correlation analysis. No type mismatches are indicated, and the duplicate row count is pending final Parquet reconciliation, though the fact that distinct=6104 on the Date column confirms every ingested date is unique, suggesting no duplicate records exist.
3. Key Column Distributions The T5YIE column tells a rich macroeconomic story through its statistics. The mean of 1.96 and median of 2.01 are closely aligned, suggesting a roughly central distribution around the Fed's informal 2% inflation target — notable in itself. However, a negative skew of -1.31 reveals a meaningful left tail, reflecting periods of deflationary fear (e.g., the 2008 financial crisis, early COVID-19 shock), which is confirmed by the striking minimum value of -2.24 — deeply negative breakevens representing acute deflation expectations. The maximum of 3.59 captures post-pandemic inflation surge periods. The IQR of Q1=1.64 to Q3=2.36 is relatively tight (0.72 spread), indicating that most of the time, markets anchor expectations near target, with 115 statistical outliers capturing the dramatic tail events that make this series analytically interesting.
4. Recommended Join Key Columns The Date column is the natural and unambiguous join key for cross-dataset correlation — it is fully populated (0 nulls), entirely distinct (6,104 unique values), and formatted as a standard date type. When joining to other datasets, analysts should account for the trading-day-only cadence of T5YIE and align on business day calendars accordingly. For datasets with lower frequency (monthly, quarterly), the Date field can be aggregated using period-end or period-average methods prior to joining.
5. Suggested Datasets for Correlation Analysis Several dataset categories would pair powerfully with this series:
- Federal Funds Rate / SOFR / Treasury Yields — Testing how monetary policy expectations drive or lag inflation breakevens is a natural and high-value analysis - CPI / PCE Inflation (BLS/BEA) — Comparing realized inflation to these expected breakevens measures forecast accuracy and market efficiency over time - Crude Oil Prices (WTI/Brent) — Energy prices are a dominant near-term inflation driver; correlation with breakevens would reveal how quickly commodity shocks are priced into expectations - S&P 500 / Equity Volatility (VIX) — Breakeven rates often spike or collapse alongside risk sentiment shifts, making equity market data a compelling pairing - USD Index (DXY) — Currency strength and inflation expectations are closely linked via import price channels, offering a multi-asset macro lens
Columns
- Date (date)
- T5YIE (decimal)
