How two macro series move together — and which one leads. Pearson cross-correlation over shared observed annual history, scanned across a ±3-year lag window so a leading/lagging relationship (e.g. growth → unemployment, Okun's law) surfaces, not just a contemporaneous one. Correlation, not causation; backward-looking, not a forecast.
How to read this:Observeda real measured value from a named source.Derivedcomputed by Predictonomy from observed series using a reproducible method.AI readan AI interpretation of already-computed figures. Opinion, not a new data point.
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Two indicators within one country, or one indicator across two economies. Every reading traces to live observed series; an unheld pair returns an honest empty state, never a fabricated number.
DerivedCorrelation inputshistory to 2026
United States — Real GDP growth vs Unemployment correlation
-0.44
best-lag r · lag −1y
moderate negativeReal GDP growth leads Unemployment · 1yn=47confidence: high
correlation by lag (years)
moderate negative relationship with Real GDP growth leading Unemployment by 1y (r=-0.44 at that lag; contemporaneous r=-0.315).
Inputs
Real GDP growthUnemployment
Pearson cross-correlation over shared observed annual history (±3y lag scan). Correlation, not causation; backward-looking, not a forecast.
