CPILFESL: Core US CPI Inflation Excluding Food and Energy — Monthly Series Since 1957

CPILFESL is the Federal Reserve Bank of St. Louis identifier for the seasonally adjusted Core Consumer Price Index — the US measure of inflation excluding volatile food and energy components. Published monthly by the Bureau of Labor Statistics since January 1957, CPILFESL strips out the most short-term-volatile categories to isolate the underlying inflation trend that monetary policy ultimately responds to. The series anchors a vast body of macroeconomic research and provides the cleanest available baseline against which headline CPI surprises are measured. Why core measures matter — and how they relate to other deflators — is detailed in the complete guide to inflation.

Dataset: US Core CPI Inflation (1957–2026) · Updated 2026-06-01

Source: FRED series CPILFESL · Federal Reserve Bank of St. Louis

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code CPILFESL:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=CPILFESL

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/us-core-cpi.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/us-core-cpi.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["core_cpi_yoy"].describe())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/us-core-cpi.csv"
df <- read_csv(url)

head(df)
summary(df$core_cpi_yoy)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

CPILFESL is the seasonally adjusted Consumer Price Index for All Urban Consumers, less Food and Energy. The Bureau of Labor Statistics constructs the underlying index from monthly price surveys covering approximately 80,000 items across 75 urban areas, then publishes both the headline and core variants. The seasonal adjustment removes predictable within-year patterns (back-to-school clothing prices, summer travel, holiday discounting) so that month-over-month movements reflect underlying inflation pressure rather than calendar effects.

The reference base period is 1982–84 = 100. Basket weights are updated biennially via the Consumer Expenditure Survey, with the most recent update incorporating post-pandemic spending patterns. Within the core basket, shelter — primarily Owner’s Equivalent Rent and Rent of Primary Residence — accounts for roughly 40% of total weight, making it the dominant driver of month-to-month core CPI movements.

This dataset is updated monthly via automated pull from the FRED API, typically within 24 hours of the BLS CPI release. The accompanying year-over-year column is computed as the 12-month log difference of CPILFESL: core_cpi_yoy = (CPILFESL[t] / CPILFESL[t-12] − 1) × 100.

Data Quality & Provider Notes

CPILFESL is one of the most rigorously curated inflation series in FRED. The BLS publishes the underlying CPI report monthly with a documented release calendar, and the seasonally adjusted core series shown here inherits that release discipline. Eco3min mirrors FRED with a daily pull, so the dataset reflects the latest BLS release within 24 hours of publication.

• Release latency. The BLS publishes the CPI report at 08:30 ET, typically between the 10th and 14th of the month for the previous month’s data. CPILFESL is updated on FRED within minutes of the BLS release.
• Revisions policy. The CPI index level is not revised after initial publication. However, BLS revises seasonal adjustment factors once per year (usually February), which can modify the entire historical seasonally adjusted series. The non-seasonally adjusted counterpart (CPILFENS) is never revised after release.
• Alternative sources. The same data is available directly from BLS (CUSR0000SA0L1E for seasonally adjusted core, CUUR0000SA0L1E for unadjusted) and through Bloomberg, Refinitiv, and Haver Analytics. ALFRED provides vintage data showing exactly what was published at each historical release date, which matters for reproducing real-time Fed reaction functions.
• Known gaps. Monthly series with no gaps since January 1957. Basket weights are updated biennially via the Consumer Expenditure Survey, which creates a methodological discontinuity that is rarely flagged in headline analyses.

For historical research, the choice between CPILFESL (seasonally adjusted) and CPILFENS (unadjusted) matters: TIPS securities and most inflation-linked financial contracts reference the unadjusted series, while macro analysis typically uses the seasonally adjusted version.

Common Pitfalls When Using CPILFESL

CPILFESL is widely cited but several recurring misinterpretations distort the signal — particularly in financial commentary that conflates it with adjacent inflation measures.

1. Confusing the index level with the YoY rate. The FRED series CPILFESL itself is the price index, not the year-over-year change. The Eco3min dataset adds the precomputed YoY rate (the core_cpi_yoy column), but users who pull CPILFESL directly from FRED sometimes plot the level expecting an inflation rate. The YoY rate is calculated as (CPILFESL[t] / CPILFESL[t-12] − 1) × 100.
2. Confusing core CPI (CPILFESL) with core PCE (PCEPILFE). The Federal Reserve targets core PCE, not core CPI. The two series typically differ by 30–50 basis points due to methodology — CPI uses fixed-weight baskets updated biennially while PCE uses chain-weighted dynamic weights. Reading core CPI as “the Fed’s inflation metric” is a common analytical shortcut that breaks down when the gap widens, as it did in 2022–2024.
3. Mixing seasonally adjusted and non-seasonally adjusted versions. CPILFESL is the seasonally adjusted series; CPILFENS is unadjusted. TIPS coupons and inflation swaps reference the unadjusted measure. Pulling CPILFESL into a TIPS-linked calculation introduces systematic seasonal bias.
4. Ignoring the shelter measurement lag. Shelter — especially Owner’s Equivalent Rent — accounts for roughly 40% of core CPI weight and lags spot market rents by 6–12 months, because the BLS surveys leases that mostly predate the reference period. CPILFESL therefore exhibits substantial momentum that does not reflect real-time housing dynamics. This structural feature has been particularly visible during the 2021–2024 cycle, when spot rents were already cooling while core CPI shelter remained elevated.

Historical Regimes

1957–1965 — Pre-acceleration stability. CPILFESL averaged 1.5–2% under the post-war Bretton Woods regime. The series begins in January 1957, capturing the tail end of the calm period before the Vietnam-era fiscal expansion ignited the Great Inflation.

1966–1982 — Great Inflation. CPILFESL accelerated from 2% to a peak above 13% in 1980, driven by sustained fiscal deficits, two oil shocks transmitting into wage demands, and a Federal Reserve that consistently underestimated inflation persistence. The 1979–1982 Volcker tightening (Fed Funds above 20% — see FEDFUNDS) ultimately broke core CPI back below 5% at the cost of two recessions, documented in our 110-year inflation history study.

1983–2007 — Great Moderation. CPILFESL stabilized in the 2–4% range, gradually converging toward 2% as Fed credibility anchored expectations. Across this window the relationship between CPILFESL and the core PCE deflator displayed a structural gap of 30–50 basis points — a divergence that has shaped Fed targeting choices since the official 2% target was adopted in 2012.

2008–2020 — Post-GFC low-flation. CPILFESL fell to a cycle low of 0.6% in October 2010 and averaged 1.8% across the decade, persistently undershooting the Fed’s 2% target despite extensive quantitative easing. This period drove the structural rethink that produced the 2020 Average Inflation Targeting framework, in which the Fed accepted overshoots to compensate for prior shortfalls.

2021–2022 — Post-pandemic core surge. CPILFESL reached 6.6% in September 2022, the highest core CPI reading since 1982. The driver mix included supply-side shocks (semiconductors, container shipping), labor market tightness, and fiscal transfers that boosted goods demand. The divergence between core and headline CPI during this episode revealed the limits of the Fed’s “look-through” doctrine.

2023–2024 — Sticky disinflation. CPILFESL declined toward 3–4% but proved markedly stickier than headline CPI, with shelter and services components remaining elevated. The disinflation path tested the Fed’s restrictive policy stance and shaped the rate cycle visible in the FEDFUNDS series.

2025–2026 — Stabilization phase. CPILFESL continues to converge toward the Fed’s implicit core target. The lag in shelter measurement, the relationship with headline PCE dynamics, and goods-services rebalancing remain the dominant analytical themes for this phase.

Related Macroeconomic Datasets

Core CPI sits at the intersection of inflation measurement, monetary policy reaction, and real-asset valuation. The following datasets contextualize CPILFESL against the metric the Fed actually targets, the policy rate it sets, and the breakeven inflation expectations the market implies.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Explore the Eco3min Dataset Hub

Sources

• U.S. Bureau of Labor Statistics — Consumer Price Index for All Urban Consumers: All Items Less Food and Energy
• Federal Reserve Bank of St. Louis — FRED series CPILFESL

Dataset Reference

Eco3min Research. (2026). CPILFESL: US Core CPI Inflation (1957–2026).<br> https://eco3min.fr/en/us-core-cpi-inflation-dataset/