Economic policy uncertainty signals (US daily + global monthly, z-score regime gauges, US-vs-world divergence)

Name: Economic policy uncertainty signals (US daily + global monthly, z-score regime gauges, US-vs-world divergence)
Creator: Economic Policy Uncertainty Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: uncertainty, policy, sentiment, risk, volatility, momentum, anomaly-detection, forecasting, signals, fred

Value-added policy-uncertainty signals derived from FRED's mirror of the Baker/Bloom/Davis Economic Policy Uncertainty indices (policyuncertainty.com): daily US EPU (1985 -> present) and monthly global EPU (1997 -> present). Each row carries change volatility, ~3-month momentum, year-over-year change, a trailing-1y uncertainty z-score with an elevated-uncertainty flag, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and the US-vs-world uncertainty divergence gauge. All computation is local pandas/numpy; no paid models or APIs.

Source: Economic Policy Uncertainty Signals (derived)15,593 lignesMis à jour: 22/09/2026

uncertaintypolicysentimentriskvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

91.8

Attribution

Baker, Bloom & Davis Economic Policy Uncertainty indices via FRED; derived signals by Frontier Data Hub

Schéma

Colonne	Type	Description
date	string	Observation date (FRED API field date; YYYY-MM-DD).
country	string
country_code	string
series_id	string	FRED series ID: USEPUINDXD (daily US EPU) or GEPUCURRENT (monthly global EPU); resolves to the series page at https://fred.stlouisfed.org/series/<id>.
series_label	string	Official FRED series title as published for the series.
value	float	Economic Policy Uncertainty index value as published (index points; higher = more policy-related economic uncertainty); see the series notes and policyuncertainty.com for methodology.
volatility_30d	float
momentum_3m	float
yoy_change_pct	float
uncertainty_z_1y	float
elevated_flag	integer
anomaly_flag	integer
forecast_1m	float
rank	integer
us_global_divergence	float

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	yoy_change_pct	uncertainty_z_1y	forecast_1m	rank	us_global_divergence
1985-01-01	United States	USA	USEPUINDXD	Economic Policy Uncertainty Index for United States	103.83	—	—	—	—	—	—	—
1985-01-02	United States	USA	USEPUINDXD	Economic Policy Uncertainty Index for United States	296.43	—	—	—	—	—	—	—
1985-01-03	United States	USA	USEPUINDXD	Economic Policy Uncertainty Index for United States	56.06	—	—	—	—	—	—	—
1985-01-04	United States	USA	USEPUINDXD	Economic Policy Uncertainty Index for United States	118.45	—	—	—	—	—	—	—
1985-01-05	United States	USA	USEPUINDXD	Economic Policy Uncertainty Index for United States	88.56	—	—	—	—	—	—	—

Télécharger un échantillon

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

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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/epu_signals/economic_policy_uncertainty_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/epu_signals/economic_policy_uncertainty_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/epu_signals/economic_policy_uncertainty_signals

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