US state coincident-activity signals (Philadelphia Fed index momentum, state-vs-nation divergence, anomalies)
Monthly signals derived from the Philadelphia Fed's Coincident Economic Activity Indexes (redistributed by FRED): 30-month annualized change volatility, 3-month momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-state volatility rank, the state-minus-nation cycle-divergence gauge and a 24-month growth z-score (the state business-cycle heat gauge). Covers the national index plus all 50 state indexes from 1979. All rows are normalized to country_code USA so they join cleanly with US macro data; the state identity rides in series_id. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: Federal Reserve Bank of Philadelphia.
Quality
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: Federal Reserve Bank of Philadelphia; derived signals by Frontier Data Hub)
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID: USPHCI for the national index, <USPS>PHCI for each state's index (e.g. TXPHCI for Texas); 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 (Federal Reserve Bank of Philadelphia data). |
| value | float | Coincident Economic Activity Index value (Index 2007=100): combines nonfarm payroll employment, the unemployment rate, average hours worked in manufacturing, and wages and salaries; the trend for each state's index is set to the trend of its gross state product. See the series notes for methodology. |
| volatility_30d | float | |
| momentum_3m | float | |
| yoy_change_pct | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| us_gap | float | |
| growth_z_24m | float |
Sample rows
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change_pct | anomaly_flag | forecast_1m | rank | us_gap | growth_z_24m |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1979-01-01 | United States | USA | AKPHCI | Coincident Economic Activity Index for Alaska | 61.24 | — | — | — | 0 | — | — | — | — |
| 1979-01-01 | United States | USA | ALPHCI | Coincident Economic Activity Index for Alabama | 50.77 | — | — | — | 0 | — | — | — | — |
| 1979-01-01 | United States | USA | ARPHCI | Coincident Economic Activity Index for Arkansas | 49.06 | — | — | — | 0 | — | — | — | — |
| 1979-01-01 | United States | USA | AZPHCI | Coincident Economic Activity Index for Arizona | 29.92 | — | — | — | 0 | — | — | — | — |
| 1979-01-01 | United States | USA | CAPHCI | Coincident Economic Activity Index for California | 40.34 | — | — | — | 0 | — | — | — | — |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/state_cycle_signals/us_state_coincident_activity_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/state_cycle_signals/us_state_coincident_activity_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)API endpoint: https://datazimuts.com/v1/datasets/state_cycle_signals/us_state_coincident_activity_signals
Tip: fetch /llms.txt for the full machine-readable catalog.