Sticky Price Consumer Price Index less Food and Energy (CORESTICKM159SFRBATL) | FRED | St. Louis Fed

Rows: 705
Columns: 2
Data range: 1968-01-01 to 2026-09-01
Auto-refresh: Re-checks its source every 14 days
Last refreshed with new data: 2026-10-25

Graph and download economic data for Sticky Price Consumer Price Index less Food and Energy (CORESTICKM159SFRBATL) from Jan 1968 to May 2026 about sticky, core, CPI, inflation, rate, price index, indexes, price, and USA.

AI analysis

Analysis: Sticky Price CPI Less Food and Energy (CORESTICKM159SFRBATL)

1. Dataset Overview and Value

This dataset, sourced directly from the Federal Reserve Bank of St. Louis's FRED API (fredgraph.csv?id=CORESTICKM159SFRBATL), tracks the Sticky Price CPI excluding Food and Energy — a core inflation measure developed by the Atlanta Fed. "Sticky price" items (as opposed to flexible-price items) are goods and services whose prices change infrequently, making this series a useful proxy for underlying inflation trends and inflation expectations, filtered further to exclude volatile food/energy components. With coverage from January 1968 through May 2026 (701 monthly observations), this is a long-running, high-frequency macroeconomic time series. Its direct provenance from FRED — a highly authoritative and actively maintained source — combined with the forward-looking date range (into 2026) suggests this is a live, regularly-updated series, making it excellent for time-series correlation studies against other economic indicators, monetary policy variables, or market indices.

2. Data Quality Observations

Data quality appears strong at first glance: zero null cells across both columns and zero nulls in either the date or value column individually, with full distinct counts (701 distinct dates, 701 distinct values) matching the row count — indicating no duplicate timestamps or repeated values, and no gaps in the join key. However, some caveats limit full confidence: the duplicate-row count is marked as pending ("Phase D — recomputed against Parquet"), and the sample data rows are empty, so verification of actual row-level content wasn't possible in this pass. The dataset is small enough (2 columns) that "type-mismatch" risk is low, but it would be worth confirming that the Decimal column truly contains no coerced/malformed strings post-Parquet conversion. Overall, structurally this is a clean, tidy time series with no missing-value concerns.

3. Key Column Distributions

The core value column (CORESTICKM159SFRBATL) spans 0.664 to 15.774, with a mean of 4.30 and median of 3.35 — the mean sitting notably above the median, consistent with the reported positive skew of 1.63, indicating a distribution with a long right tail (likely driven by inflation spikes such as the early 1980s and 2021-2022 periods). The interquartile range (Q1=2.48, Q3=5.12) shows most observations cluster in a relatively narrow 2.5–5 point band, while 51 outliers (~7.3% of observations) reflect the historically rare but impactful high-inflation episodes. This right-skewed, outlier-rich distribution is important for correlation analysis: linear correlation methods may be distorted by these extreme values, so rank-based (Spearman) correlation or outlier-aware windowing may yield more robust results than Pearson correlation alone.

4. Recommended Join Keys

The obvious and only viable join key is observationdate — a fully populated, monthly-granularity date field with zero nulls and full distinct coverage across the 1968–2026 span. For cross-dataset correlation, this should be aligned to other FRED series (which typically share the same monthly date convention) or resampled/aggregated when joining against quarterly (e.g., GDP) or daily (e.g., market indices, Treasury yields) datasets. Care should be taken to standardize date alignment (start-of-month vs. end-of-month conventions) when merging with non-FRED sources.

5. Suggested Pairings for Correlation Discovery

Strong candidates for correlation analysis include: headline CPI and PCE inflation indices (to compare sticky vs. flexible price dynamics), Federal Funds Rate / effective interest rates (to study monetary policy transmission lags), unemployment rate (U-3) (Phillips curve relationships), wage growth series (e.g., Atlanta Fed Wage Growth Tracker), 5-year/10-year breakeven inflation rates (market-based inflation expectations vs. this backward-looking measure), and M2 money supply growth. Given the long historical range, this series also pairs well with recession indicator datasets (NBER dates) to examine sticky inflation behavior around business cycle turning points.

Columns

observation_date (date)
CORESTICKM159SFRBATL (decimal)