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US sticky vs flexible inflation signals (underlying inflation gauge, persistence regime)

Monthly signals derived from the Atlanta Fed's sticky-price and flexible-price CPI series (redistributed by FRED, 1968 ->; both series are published as 12-month percent changes): 30-month annualized point-change volatility, 3-month point-change momentum, the 12-month percent change itself, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-series volatility rank, the sticky-minus-flexible spread (pp — the underlying-inflation gauge) and a 10-year sticky-inflation z-score (the persistence-regime gauge). All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: Federal Reserve Bank of Atlanta.

Source: Sticky vs Flexible Inflation Signals (derived)1,408 lignesMis à jour: 22/09/2026
inflationsticky-pricescore-inflationcpimonetary-policyvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

99.4

Attribution

Federal Reserve Bank of St. Louis (FRED; underlying data: Federal Reserve Bank of Atlanta; derived signals by Frontier Data Hub)

Schéma

ColonneTypeDescription
datestringObservation date (FRED API field date; YYYY-MM-DD).
countrystring
country_codestring
series_idstringFRED series ID (CORESTICKM159SFRBATL for sticky-price CPI less food and energy, FLEXCPIM159SFRBATL for flexible-price CPI); IDs resolve to the series page at https://fred.stlouisfed.org/series/<id>.
series_labelstringOfficial FRED series title as published for the series (Federal Reserve Bank of Atlanta sticky/flexible price CPI data).
valuefloat12-month percent change (percent), monthly, seasonally adjusted — the Atlanta Fed publishes these series as percent change from year ago, not index levels; see the series notes for the sticky/flexible classification methodology.
volatility_30dfloat
momentum_3mfloat
yoy_change_pctfloat
anomaly_flaginteger
forecast_1mfloat
rankinteger
sticky_flexible_spreadfloat
sticky_z_10yfloat

Exemple de lignes

datecountrycountry_codeseries_idseries_labelvaluevolatility_30dmomentum_3myoy_change_pctanomaly_flagforecast_1mranksticky_flexible_spreadsticky_z_10y
1968-01-01United StatesUSACORESTICKM159SFRBATLSticky Price Consumer Price Index less Food and Energy3.65186106——3.651861060——0.5697291179999997—
1968-01-01United StatesUSAFLEXCPIM159SFRBATLFlexible Price Consumer Price Index3.082131942——3.0821319420——0.5697291179999997—
1968-02-01United StatesUSACORESTICKM159SFRBATLSticky Price Consumer Price Index less Food and Energy3.673819411——3.6738194110——0.41336798500000027—
1968-02-01United StatesUSAFLEXCPIM159SFRBATLFlexible Price Consumer Price Index3.260451426——3.2604514260——0.41336798500000027—
1968-03-01United StatesUSACORESTICKM159SFRBATLSticky Price Consumer Price Index less Food and Energy4.142163975——4.1421639750——0.5723831349999999—

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Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/sticky_inflation_signals/us_sticky_vs_flexible_inflation_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/sticky_inflation_signals/us_sticky_vs_flexible_inflation_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/sticky_inflation_signals/us_sticky_vs_flexible_inflation_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

US sticky vs flexible inflation signals (underlying inflation gauge, persistence regime)