Macro Regime Classification: Methodology and Data

What this classification computes

For any given month, the classification answers: what macro regime is the US economy in, based only on public data and a verifiable rule?

Every term is verifiable: the regime name follows from the growth × inflation grid (see Architecture), the financial conditions qualifier from the Chicago Fed’s NFCI, the global context from OECD CLIs and the ECB’s CISS.

What this classification is NOT

• A proprietary score (no opaque weighting)
• A trading signal or investment recommendation
• A forecast of future conditions
• An official cycle dating (≠ NBER)
• A portfolio timing or allocation system

Three independent axes

The classification uses three independent axes — real activity, inflation, financial conditions — rather than a flat list that conflates orthogonal dimensions. These three axes are the inputs of the calculation: what the engine measures to produce a verdict. They must not be confused with the three layers of the Atlas (see below), which describe the nature of the regime objects, at different horizons and evidentiary statuses. Two distinct reading grids: the axes are the ingredients, the layers are the dishes.

The 3×3 growth × inflation grid maps to seven named regimes plus a transition state.

A hierarchical world view, not an aggregated score

A single aggregated world score masks inter-zone divergence (e.g., 2022: US tightening vs China easing) — a misleading indicator. The architecture is hierarchical:

The eurozone will become a second classified core (same architecture, ECB/Eurostat inputs) in version 1.1. The code is parameterized so that adding a zone is a configuration parameter, not a rewrite.

The seven regimes and the transition state

The overlay prefixes the name: “Disinflationary Contraction under acute financial stress”, “Balanced Expansion under accommodating conditions”.

Three axes in, three layers out

The previous section describes the seven regimes of the G × I grid: this is layer 1 of the Atlas, the one the engine computes in real time. But the Atlas of regimes contains two other kinds of objects, read at different time horizons and with different evidentiary status. To avoid confusion: the three axes above are the inputs of the calculation (growth, inflation, financial conditions); the three layers below are the nature of the regime objects. They are not two names for the same thing.

US core and global context inputs

US Core

* HY OAS: optional corroboration only. Since April 2026, FRED truncates ICE BofA series to 3 rolling years. Included only when a full pre-April 2026 historical fixture is available locally.

Global context layer

Fixed institutional thresholds

These thresholds are not Eco3min editorial choices — they are defined by the producing institutions:

Calibrated thresholds

Other thresholds (CFNAI-MA3 levels, Trimmed Mean PCE bands, NFCI overlay levels) are calibrated by percentile rank on the full available history, then validated against reference episodes: 2008 GFC, 2020 COVID, 2021 reflation, 2022 tightening, 2023–2024 soft landing.

Any threshold modification receives a version number and a changelog entry. The full table is published in the GitHub repository (thresholds.json, versioned in semver).

Hysteresis: preventing regime oscillation

Without a hysteresis mechanism, an indicator oscillating around a threshold would produce unreadable regime changes month after month. The rule:

Two resolution windows

What this system cannot do

• Revisions: the current month’s regime may change after data source revisions (CFNAI is regularly revised). The JSON timestamps the computation and flags lagged inputs.
• Publication lags: the CFNAI publishes with ~3-week lag. The classification uses the last known value and documents the pending input in the JSON.
• OECD CLI lag: ~6 weeks. The global context layer updates slower than the US core and may delay detection of inter-zone divergence.
• Not a forecast: the classification describes the currently measured state, not future conditions.
• Energy supply shocks: the Trimmed Mean PCE excludes volatile components. An oil spike can be invisible on the inflation axis while headline CPI surges. The headline_underlying_divergence flag in the JSON signals this case automatically.
• Structural regimes not computed: layer 3 regimes (secular stagnation, financial repression, fiscal dominance) are not produced by the engine. They are long-run editorial frameworks and must not be read as a measured monthly verdict.

Any third party can recalculate the regime

Any third party can recalculate the regime from primary institutional sources and the published threshold table, with no opaque component. The Python code and thresholds are published open source.

GitHub repository: [URL to be added after deployment]
Files: regime_classifier.py, thresholds.json (semver versioned), data/regime_history.csv, requirements.txt, LICENSE (MIT code, CC-BY 4.0 derived data).