OECD business-cycle signals (CLI turning points, trend gaps, expansion/contraction phases)

Name: OECD business-cycle signals (CLI turning points, trend gaps, expansion/contraction phases)
Creator: OECD Business-Cycle Signals (derived)
License: https://www.oecd.org/en/about/terms-and-conditions.html
Keywords: business-cycle, leading-indicator, cli, oecd, turning-points, recession, expansion, momentum, anomaly-detection, forecasting, signals, global

Signals derived from the OECD Composite Leading Indicator (amplitude adjusted, monthly): CLI distance from long-term trend (cli_gap vs 100), 3/6-month momentum, turning-point detection (peaks/troughs of the smoothed CLI), expansion/contraction phase flags, below-trend streaks, 30-month annualized volatility of CLI changes, 3-sigma anomaly flags, naive-drift 1-month forecasts and a per-month cross-country volatility rank. Covers 18 countries (AUS, BRA, CAN, CHN, DEU, ESP, FRA, GBR, IDN, IND, ITA, JPN, KOR, MEX, TUR, USA, ZAF plus aggregates G20, G7, NAFTA, A5M, G4E), monthly 1955 -> present. All rows carry canonical country_code so they join cleanly with country-keyed macro data. Raw series: OECD Data Explorer (Main Economic Indicators).

Source: OECD Business-Cycle Signals (derived)14,263 rowsUpdated: 9/22/2026

business-cycleleading-indicatorclioecdturning-pointsrecessionexpansionmomentumanomaly-detectionforecastingsignalsglobal

Quality

96.7

Attribution

OECD (derived signals by Frontier Data Hub)

Schema

Column	Type	Description
date	string	Observation month (OECD MEI time period; YYYY-MM-DD, first of month).
country	string
country_code	string
series_id	string	OECD MEI series key, e.g. USA.CLI_AA: country/aggregate code + CLI_AA (Composite Leading Indicator, amplitude adjusted).
series_label	string	Country or aggregate name as published by the OECD for the CLI series.
value	float	Composite Leading Indicator, amplitude adjusted (OECD MEI). Index with long-term average = 100; values above 100 signal above-trend activity. See the OECD CLI methodology notes for component series and revisions.
cli_gap	float
momentum_3m	float
momentum_6m	float
turning_point	string
phase	string
below_trend_streak	integer
volatility_30d	float
anomaly_flag	integer
forecast_1m	float
rank	integer

Sample rows

date	country	country_code	series_id	series_label	value	cli_gap	momentum_3m	momentum_6m	turning_point	volatility_30d	forecast_1m	rank
1955-01-01	United States	USA	USA.CLI_AA	United States	101.4665	1.4664999999999964	—	—	trough	—	—	—
1955-02-01	NAFTA	NAFTA	NAFTA.CLI_AA	NAFTA	102.8839	2.883899999999997	—	—	trough	—	—	—
1955-02-01	United States	USA	USA.CLI_AA	United States	101.8199	1.819900000000004	—	—		—	—	—
1955-03-01	NAFTA	NAFTA	NAFTA.CLI_AA	NAFTA	103.1792	3.1791999999999945	—	—		—	—	—
1955-03-01	United States	USA	USA.CLI_AA	United States	102.1122	2.1122000000000014	—	—		—	—	—

Download sample data

Download the full sample snapshot for this dataset (sample rows, not the complete dataset).

Download sample CSV Download sample JSON

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/oecd_cycle_signals/oecd_business_cycle_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/oecd_cycle_signals/oecd_business_cycle_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/oecd_cycle_signals/oecd_business_cycle_signals

Tip: fetch /llms.txt for the full machine-readable catalog.