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US GDP and output-gap signals (growth, slack, investment/consumption shares)

Quarterly US national-accounts signals derived from FRED: real GDP growth (QoQ annualized and YoY), the CBO output gap (actual vs potential GDP) with its 10-year z-score, investment and consumption shares of GDP, the GDP-deflator inflation gauge, plus 30-quarter annualized change volatility, 1-quarter momentum, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts and a per-quarter cross-series volatility rank. 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: U.S. Bureau of Economic Analysis and the Congressional Budget Office.

Source: US GDP & Output-Gap Signals (derived)1,582 lignesMis à jour: 22/09/2026
gdpoutput-gappotential-gdpinvestmentconsumptionnational-accountsrecessionvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

99.3

Attribution

Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Economic Analysis and Congressional Budget Office; derived signals by Frontier Data Hub)

Schéma

ColonneTypeDescription
datestringObservation date (FRED API field date; YYYY-MM-DD, first day of the reference quarter).
countrystring
country_codestring
series_idstringFRED series ID, e.g. GDPC1, GDPPOT, GDPCTPI, GPDI, PCEC; resolves to the series page at https://fred.stlouisfed.org/series/<id>.
series_labelstringOfficial FRED series title as published for the series (U.S. Bureau of Economic Analysis national accounts; GDPPOT: Congressional Budget Office).
valuefloatObservation value as published by FRED for this series: billions of chained 2017 dollars at a seasonally adjusted annual rate (GDPC1, GPDI, PCEC); billions of chained 2017 dollars (GDPPOT); index, 2017=100 (GDPCTPI); see the series notes for methodology and revisions.
growth_qoq_annfloat
yoy_change_pctfloat
volatility_30dfloat
momentum_3mfloat
anomaly_flaginteger
forecast_1mfloat
rankinteger
output_gapfloat
gap_z_10yfloat
investment_sharefloat
consumption_sharefloat
deflator_yoyfloat

Exemple de lignes

datecountrycountry_codeseries_idseries_labelvaluegrowth_qoq_annyoy_change_pctvolatility_30dmomentum_3manomaly_flagforecast_1mrankoutput_gapgap_z_10yinvestment_shareconsumption_sharedeflator_yoy
1947-01-01United StatesUSAGDPC1Real Gross Domestic Product2182.68101.64265873024963347.154549840311067
1947-01-01United StatesUSAGDPCTPIGross Domestic Product: Chain-type Price Index11.14801.64265873024963347.154549840311067
1947-01-01United StatesUSAGPDIGross Private Domestic Investment35.85401.64265873024963347.154549840311067
1947-01-01United StatesUSAPCECPersonal Consumption Expenditures156.16101.64265873024963347.154549840311067
1947-04-01United StatesUSAGDPC1Real Gross Domestic Product2176.892-1.0566839487050617-5.78900000000021501.58505796337163287.351352294923222

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

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/gdp_signals/us_gdp_output_gap_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/gdp_signals/us_gdp_output_gap_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/gdp_signals/us_gdp_output_gap_signals

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

US GDP and output-gap signals (growth, slack, investment/consumption shares)