US household balance signals (savings buffer, debt-service strain, savings/dsr anomalies)
Monthly/quarterly signals derived from BEA and Federal Reserve household-finances data (redistributed by FRED): 30-period annualized change volatility, ~3-month momentum, year-over-year change in percentage points, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and the household savings buffer (saving rate minus debt service ratio — the net cash-buffer gauge). Covers the personal saving rate and the household debt service ratio. 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 Board of Governors of the Federal Reserve System.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Economic Analysis and Board of Governors of the Federal Reserve System; 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 (PSAVERT, TDSP); FRED IDs resolve to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (U.S. Bureau of Economic Analysis and Board of Governors of the Federal Reserve System data). |
| value | float | Observation value as published by FRED: personal saving rate as a percent of disposable personal income (PSAVERT); household debt service payments as a percent of disposable personal income (TDSP). See the series notes for methodology. |
| volatility_30d | float | |
| momentum_3m | float | |
| yoy_change | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| savings_buffer | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change | anomaly_flag | forecast_1m | rank | savings_buffer |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1959-01-01 | United States | USA | PSAVERT | Personal Saving Rate | 11.3 | — | — | — | 0 | — | — | — |
| 1959-02-01 | United States | USA | PSAVERT | Personal Saving Rate | 10.6 | — | — | — | 0 | — | — | — |
| 1959-03-01 | United States | USA | PSAVERT | Personal Saving Rate | 10.3 | — | — | — | 0 | — | — | — |
| 1959-04-01 | United States | USA | PSAVERT | Personal Saving Rate | 11.2 | — | -0.10000000000000142 | — | 0 | — | — | — |
| 1959-05-01 | United States | USA | PSAVERT | Personal Saving Rate | 10.6 | — | 0 | — | 0 | — | — | — |
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/household_balance_signals/us_household_balance_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/household_balance_signals/us_household_balance_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/household_balance_signals/us_household_balance_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.